Do I need proxies for podcast analytics and RSS feed scraping?

For a few hundred feeds, no. Once you are polling thousands of feeds concentrated on a handful of large hosting providers, per-IP throttling and CDN bot rules make a rotating pool necessary, and conditional requests matter more than raw IP count. Chart collection is the part that genuinely needs distributed IPs.

Why do podcast chart rankings differ by country?

Each Apple storefront and each Spotify country chart is computed only from activity in that market. A show with a concentrated regional audience can rank top ten in one storefront and be absent everywhere else, so podcast chart rankings by country are separate datasets, not variations of one list.

Can you scrape podcast download numbers?

No. Downloads are counted server-side by the hosting provider under the IAB Tech Lab measurement guidelines, and listener data sits behind owner authentication in Apple Podcasts Connect and Spotify for Creators. Any public download figure is a model fitted on chart, review and cadence signals.

What is the Podcasting 2.0 namespace and is it worth parsing?

It is a community-maintained XML extension adding transcripts, chapters, funding links, named people and a stable podcast:guid. Adoption is partial, but it sits in feeds you fetch anyway, so parsing it costs nothing and podcast:guid alone fixes cross-platform deduplication.

How do I detect when a podcast publishes a new episode?

Poll with If-None-Match and If-Modified-Since so unchanged feeds return a 304, subscribe to WebSub hubs or Podping where hosts support them, and on any change compare episode GUID sets to find additions and removals rather than diffing the XML.

Is it legal to collect podcast data from public feeds?

Reading public RSS metadata and public chart pages is ordinary collection of published information. Downloading the copyrighted audio, redistributing transcripts, or accessing an owner's private analytics dashboard are separate acts with separate legal exposure, so keep your pipeline to metadata.

Use Cases

Proxies for Podcast Analytics and Media Data

How proxies for podcast analytics collect RSS feeds, Podcasting 2.0 tags and per-country chart rankings, and the download numbers you honestly cannot scrape.

S SparkProxyAug 28, 2026 - 06:30 0 21 min read

Proxies for podcast analytics exist because podcast data is split down the middle: the RSS layer is genuinely open and machine-readable, while charts, rankings and availability sit inside per-country Apple and Spotify storefronts that return a different answer depending on where your request comes from.

That split decides your whole architecture. Feeds you can parse with an XML library and a polite crawler. Charts you have to collect roughly 175 times, once per storefront, because a show sitting at #4 in Ireland may not chart at all in the United States. And the number everyone actually wants, downloads, is not on the public internet in any form.

Key Takeaways

Podcast RSS is an open, documented format. The Podcasting 2.0 namespace adds transcripts, chapters, funding links and a stable podcast:guid that survives feed URL changes.

Chart position is storefront-specific. You must collect per country, and Apple exposes a clean public JSON endpoint per storefront that most teams never find.

Episode and catalogue change detection runs on conditional HTTP requests plus GUID set comparison, not on refetching millions of feeds every hour.

Real download and listener counts live with the hosting provider and the platform. They cannot be scraped. Anything sold as a "download estimate" is an inference from public popularity signals.

What Podcast Data Actually Exists in Public

Sort the field into three tiers before writing any code, because each tier needs a different collection method and carries a different confidence level.

Tier	Examples	Where it lives	Collection method
Open and authoritative	Titles, descriptions, episode list, publish dates, durations, categories, transcript links, funding links	The show's own RSS feed, served over plain HTTP	Fetch and parse XML
Public but platform-specific	Chart rank, category rank, storefront availability, artwork, ratings and review counts	Apple Podcasts and Spotify surfaces, one result set per country	Per-storefront collection, geo-aware
Private	Downloads, unique listeners, completion rate, drop-off curve, demographics	The hosting provider's servers and each platform's creator dashboard	Not available. Full stop.

Tier one is the reason podcasting stayed an open ecosystem while video consolidated. A podcast is, structurally, an RSS 2.0 document with an element pointing at an audio file. The RSS 2.0 specification has not meaningfully changed since 2003, and Apple's podcast feed requirements layer an itunes: namespace on top of it rather than replacing it. Nobody needs an API key to read a feed. That is by design.

Tier two is where proxies earn their place. Tier three is where honest analytics products draw a line that their competitors often blur, and we will get to that line.

Feed Discovery: Finding the RSS Behind a Show

You almost never start with a feed URL. You start with a show name, an Apple collection ID, or a Spotify URI, and you need to resolve it to the canonical RSS.

Route 1: the iTunes Lookup API

Apple runs a free, unauthenticated JSON endpoint that returns feedUrl for any podcast collection ID. It is documented in Apple's iTunes Search API reference.

curl -s "https://itunes.apple.com/lookup?id=1200361736&entity=podcast"

The response carries collectionName, artistName, genres, trackCount and, critically, feedUrl. Two things bite people here. Apple documents a soft limit of roughly 20 calls per minute per IP, and the results are storefront-sensitive: add &country=DE and a show unavailable in Germany returns resultCount: 0. That second behaviour is useful, since it doubles as an availability probe.

Route 2: the Podcast Index API

The open Podcast Index API indexes several million feeds and supports lookup by Apple ID, feed URL, GUID or search term. Auth is an odd but simple scheme: a SHA-1 of your API key, secret and current Unix timestamp in the Authorization header.

import hashlib, time, requests

KEY, SECRET = "your-key", "your-secret"
now = str(int(time.time()))
auth = hashlib.sha1((KEY + SECRET + now).encode()).hexdigest()

r = requests.get(
    "https://api.podcastindex.org/api/1.0/podcasts/byitunesid",
    params={"id": 1200361736},
    headers={
        "User-Agent": "SparkProxyResearch/1.0",
        "X-Auth-Key": KEY,
        "X-Auth-Date": now,
        "Authorization": auth,
    },
    timeout=30,
)
feed = r.json()["feed"]
print(feed["url"], feed["podcastGuid"])

Route 3: the show's own website

Well-built podcast sites still advertise the feed in the document head:

<link rel="alternate" type="application/rss+xml" title="Feed" href="https://feeds.sparkproxy.io/show.xml">

Worth checking, because it sometimes reveals the raw host feed behind a tracking-prefixed URL that Apple displays instead.

Spotify is the awkward one. An open.spotify.com/show/... URI does not resolve to an RSS feed through any public API, because Spotify deliberately does not expose the underlying feed. Matching a Spotify show to its RSS means fuzzy matching on title plus publisher plus the first episode's publish date, and you should carry a confidence score on that join rather than pretending it is exact.

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Parsing the Feed: iTunes Tags and the Real Structure

A podcast feed is one with N elements. The fields that matter for analytics are a mix of core RSS and the itunes: namespace:

Field	Level	Why it matters
``, `<itunes:author>`</td><td>Channel</td><td>Entity resolution across platforms</td></tr><tr><td>`<itunes:type>`</td><td>Channel</td><td>`episodic` or `serial`, changes how you read episode order</td></tr><tr><td>`<itunes:category>`</td><td>Channel</td><td>Apple's taxonomy, drives which chart the show competes in</td></tr><tr><td>`<guid>`</td><td>Item</td><td>The only stable episode identifier; `isPermaLink` is often a lie</td></tr><tr><td>`<pubDate>`</td><td>Item</td><td>RFC 822 format, frequently wrong or timezone-naive</td></tr><tr><td>`<enclosure url length type>`</td><td>Item</td><td>Audio URL, byte size, MIME type. Do not download it</td></tr><tr><td>`<itunes:duration>`</td><td>Item</td><td>Seconds, or `HH:MM:SS`, or `MM:SS`. Handle all three</td></tr><tr><td>`<itunes:episode>`, `<itunes:season>`</td><td>Item</td><td>Real ordering when `pubDate` is unreliable</td></tr></tbody></table></div> <pre><code class="language-python">import feedparser feed = feedparser.parse(xml_body) # xml_body = raw XML from your fetcher show = { "title": feed.feed.get("title"), "author": feed.feed.get("author"), "type": feed.feed.get("itunes_type", "episodic"), "categories": [t["term"] for t in feed.feed.get("tags", [])], "episode_count": len(feed.entries), } episodes = [{ "guid": e.get("id"), "title": e.get("title"), "published": e.get("published_parsed"), "duration": e.get("itunes_duration"), "audio_bytes": int(e.enclosures[0].get("length", 0)) if e.enclosures else None, } for e in feed.entries] </code></pre> <p>Two field-level gotchas that cost people a week each. First, <code><itunes:duration></code> has three legal formats in the wild and a nontrivial number of feeds report zero. Second, the <code>length</code> attribute on <code><enclosure></code> is supposed to be the file size in bytes, but plenty of hosts emit <code>0</code> or a stale value, so never treat it as a reliable audio-length proxy. If you need duration you need the tag, and if the tag is missing you accept a null rather than infer one.</p> <p>Feeds are also frequently large. A long-running daily show can carry 3,000 items in a single XML document running past 15 MB. Stream-parse with <code>lxml.etree.iterparse</code> rather than loading the whole tree if you are sweeping a wide catalogue.</p></div><!--[--><!--]--></section><!----><!--]--><!--[--><section id="the-podcasting-20-namespace" class="scroll-mt-28"><span class="mt-12 block h-1 w-10 rounded-full bg-brand" aria-hidden="true"></span><h2 class="group/h relative font-extrabold text-navy mb-4 mt-3 text-2xl md:text-[1.7rem]"><a href="#the-podcasting-20-namespace" class="absolute -left-6 top-1 hidden text-brand opacity-0 transition-opacity group-hover/h:opacity-100 lg:inline" aria-hidden="true">#</a> The Podcasting 2.0 Namespace</h2><!----><div class="blog-content"><p>This is the part most media analytics stacks skip, and it is free structured data sitting in feeds they already fetch.</p> <p>The <a rel="noopener noreferrer" target="_blank" href="https://github.com/Podcastindex-org/podcast-namespace/blob/main/docs/1.0.md">Podcast Namespace</a> is a community-maintained extension, declared as <code>xmlns:podcast="https://podcastindex.org/namespace/1.0"</code>. Adoption is partial but rising, and the tags that actually appear in production feeds are worth handling:</p> <div class="table-wrap"><table><thead><tr><th>Tag</th><th>Level</th><th>What you get</th></tr></thead><tbody><tr><td>`podcast:guid`</td><td>Channel</td><td>A stable show identifier that survives a feed URL change</td></tr><tr><td>`podcast:transcript`</td><td>Item</td><td>URL plus MIME type for a VTT, SRT or JSON transcript</td></tr><tr><td>`podcast:chapters`</td><td>Item</td><td>JSON chapter file with segment titles and timestamps</td></tr><tr><td>`podcast:person`</td><td>Both</td><td>Named hosts and guests with roles, the closest thing to a guest graph</td></tr><tr><td>`podcast:funding`</td><td>Channel</td><td>Where the show monetises, a direct business-model signal</td></tr><tr><td>`podcast:location`</td><td>Both</td><td>Geographic subject of the show or episode</td></tr><tr><td>`podcast:locked`</td><td>Channel</td><td>Whether the owner has blocked automated feed migration</td></tr><tr><td>`podcast:medium`</td><td>Channel</td><td>`podcast`, `music`, `audiobook`, `newsletter`, distinguishes format</td></tr></tbody></table></div> <p><code>podcast:guid</code> deserves special attention. The spec defines it as a UUID version 5 generated over the feed URL with the protocol scheme and any trailing slashes stripped, using the fixed namespace UUID <code>ead4c236-bf58-58c6-a2c6-a6b28d128cb6</code>. That means you can compute the expected GUID for any feed yourself and use it as a join key, even for feeds that do not publish the tag:</p> <pre><code class="language-python">import uuid PODCAST_NS = uuid.UUID("ead4c236-bf58-58c6-a2c6-a6b28d128cb6") def podcast_guid(feed_url: str) -> str: stripped = feed_url.split("://", 1)[-1].rstrip("/") return str(uuid.uuid5(PODCAST_NS, stripped)) print(podcast_guid("https://feeds.sparkproxy.io/show.xml")) </code></pre> <p>For a media analytics product, that one function solves the deduplication problem that otherwise haunts every podcast dataset: the same show reachable through four different tracking-prefixed URLs, counted four times.</p> <p><code>podcast:person</code> is the sleeper. If you track guest appearances across a media landscape, the way you would track byline networks in <a href="https://www.sparkproxy.io/blog/proxies-for-news-and-media-monitoring/">news and media monitoring</a>, the tag hands you a name, a role and often a URL without any named entity recognition at all.</p></div><!--[--><!--]--></section><!----><!--]--><!--[--><section id="why-chart-position-must-be-collected-per-country" class="scroll-mt-28"><span class="mt-12 block h-1 w-10 rounded-full bg-brand" aria-hidden="true"></span><h2 class="group/h relative font-extrabold text-navy mb-4 mt-3 text-2xl md:text-[1.7rem]"><a href="#why-chart-position-must-be-collected-per-country" class="absolute -left-6 top-1 hidden text-brand opacity-0 transition-opacity group-hover/h:opacity-100 lg:inline" aria-hidden="true">#</a> Why Chart Position Must Be Collected Per Country</h2><!----><div class="blog-content"><p>Here is the fact that reshapes the pipeline: there is no such thing as "the podcast chart". There are roughly 175 Apple storefronts, each with its own Top Shows list and its own per-category lists, and they diverge wildly.</p> <p>A show with a concentrated audience in a small market routinely charts top ten there and nowhere else. A US-heavy show may be invisible in every other storefront. Ranking is also relative to a storefront's total listening volume, so #20 in the United States and #1 in Iceland are not comparable achievements, and any dashboard that averages ranks across countries into one number is producing a meaningless figure.</p> <p>Apple does not publish the ranking formula. What it has said publicly is that the Top Shows chart reflects recent follower and listener activity rather than lifetime download totals, which is why a show can drop 40 places in a week without losing a single subscriber. Treat rank as a momentum signal, not a size signal.</p> <h4 id="apple-s-public-per-storefront-json">Apple's public per-storefront JSON</h4> <p>Most teams scrape <code>podcasts.apple.com</code> HTML for this. You usually do not have to. This is the classic case for <a href="https://www.sparkproxy.io/blog/how-to-scrape-hidden-json-api-endpoints/">finding the hidden JSON endpoint</a> behind a rendered page: Apple's Marketing Tools RSS service publishes chart data as clean JSON, keyed by storefront in the path.</p> <pre><code class="language-bash"># Top 100 shows in the US storefront curl -s "https://rss.marketingtools.apple.com/api/v2/us/podcasts/top/100/podcasts.json" # The same list for Germany curl -s "https://rss.marketingtools.apple.com/api/v2/de/podcasts/top/100/podcasts.json" </code></pre> <p>Each entry carries the collection ID, name, artist and artwork URL. Pair that ID with the Lookup call from earlier and you have chart rank joined to a feed URL. Sweeping every storefront daily is a few hundred requests, which is exactly the volume where a single origin IP starts collecting 403s and where distributed egress stops being optional.</p> <h4 id="spotify-and-the-rest">Spotify and the rest</h4> <p>Spotify's Web API has <a rel="noopener noreferrer" target="_blank" href="https://developer.spotify.com/documentation/web-api/reference/get-a-show">Get Show</a> and episode endpoints, requires OAuth client credentials, and takes a <code>market</code> parameter that changes availability but does not return chart position. Spotify's charts live on a separate public web property, <a rel="noopener noreferrer" target="_blank" href="https://podcastcharts.byspotify.com/">podcastcharts.byspotify.com</a>, broken out by country and refreshed weekly rather than daily. Different cadence, different methodology, not comparable to Apple's numbers.</p> <p>So your chart table needs a composite key of <code>(platform, storefront, chart_type, category, captured_at)</code>. Anyone who models it as <code>show_id -> rank</code> has already lost the data.</p> <h4 id="where-the-ip-actually-matters">Where the IP actually matters</h4> <p>Be precise about this, because vendors oversell it. The Apple Marketing Tools endpoint takes the storefront in the URL, so a German IP is not required to read the German chart. What geo-distributed IPs genuinely buy you:</p> <ol> <li><strong>Rate limit headroom.</strong> 175 storefronts multiplied by dozens of categories, run daily, is tens of thousands of requests from one address.</li> <li><strong>Availability checks.</strong> Whether a show is listed at all in a storefront, and what artwork or description a local listener sees, is served by IP-inferred surfaces on the web properties.</li> <li><strong>Localised web pages.</strong> <code>podcasts.apple.com</code> and Spotify's web player render country-specific copy, subscription pricing, and different recommendation modules.</li> <li><strong>Region-restricted catalogue.</strong> Rights-limited and explicit-content-filtered shows disappear entirely from certain storefronts, and that disappearance is only observable from inside.</li> </ol> <p>Points 2 through 4 are the same problem covered in <a href="https://www.sparkproxy.io/blog/what-does-geo-targeting-mean-in-proxies/">geo-targeting with proxies</a>, and they are why a podcast analytics stack looks structurally like an app store data pipeline. If you have built <a href="https://www.sparkproxy.io/blog/scrape-app-store-and-google-play-data/">App Store and Google Play collection</a> before, the storefront fan-out pattern will feel familiar.</p></div><!--[--><!--]--></section><!----><!--]--><!--[--><section id="episode-and-catalogue-change-detection" class="scroll-mt-28"><span class="mt-12 block h-1 w-10 rounded-full bg-brand" aria-hidden="true"></span><h2 class="group/h relative font-extrabold text-navy mb-4 mt-3 text-2xl md:text-[1.7rem]"><a href="#episode-and-catalogue-change-detection" class="absolute -left-6 top-1 hidden text-brand opacity-0 transition-opacity group-hover/h:opacity-100 lg:inline" aria-hidden="true">#</a> Episode and Catalogue Change Detection</h2><!----><div class="blog-content"><p>Refetching millions of feeds on a fixed schedule is wasteful and gets you throttled. Change detection is the difference between a pipeline that costs a fortune and one that does not.</p> <h4 id="conditional-requests-first">Conditional requests first</h4> <p>Podcast hosts are unusually good about HTTP caching headers, because they serve feeds to millions of podcast apps. Use them. <a rel="noopener noreferrer" target="_blank" href="https://www.rfc-editor.org/rfc/rfc9110#name-conditional-requests">RFC 9110 conditional requests</a> define the contract:</p> <pre><code class="language-python">import requests def poll(feed_url, etag=None, last_modified=None): headers = {"User-Agent": "SparkProxyPodcastBot/1.0 (+https://www.sparkproxy.io/bot)"} if etag: headers["If-None-Match"] = etag if last_modified: headers["If-Modified-Since"] = last_modified r = requests.get(feed_url, headers=headers, timeout=45) if r.status_code == 304: return None, etag, last_modified # unchanged, a few hundred bytes spent return r.text, r.headers.get("ETag"), r.headers.get("Last-Modified") </code></pre> <p>In a large sweep the majority of daily polls come back 304. That single header pair cuts bandwidth dramatically and drops your visible request weight enough that hosts stop noticing you.</p> <h4 id="push-instead-of-poll">Push instead of poll</h4> <p>Two mechanisms let you skip polling for participating hosts. <a rel="noopener noreferrer" target="_blank" href="https://www.w3.org/TR/websub/">WebSub</a> is advertised in the feed as <code><atom:link rel="hub" href="https://www.sparkproxy.io/blog/..."></code> and gives you a callback on publish. <a rel="noopener noreferrer" target="_blank" href="https://podping.org/">Podping</a> is podcast-specific: hosts write a lightweight notification when a feed updates, and consumers watch that stream instead of crawling. Neither covers the whole ecosystem, so they reduce your polling load rather than replacing it.</p> <h4 id="diffing-the-catalogue">Diffing the catalogue</h4> <p>When a feed does change, compare GUID sets rather than diffing raw XML:</p> <pre><code class="language-python">def diff_catalogue(previous_guids: set, current: list) -> dict: current_guids = {e["guid"] for e in current} return { "new": sorted(current_guids - previous_guids), "removed": sorted(previous_guids - current_guids), "retained": len(current_guids & previous_guids), } </code></pre> <p>The <code>removed</code> bucket is the interesting one, and it is the metric nobody tracks. Episodes vanish from feeds constantly: sponsorship expiry, legal takedowns, quiet retractions after a bad guest, or a host truncating the feed to the most recent 300 items. A silent unpublish is often a genuine news event, and you only catch it if you retain your own history of the catalogue.</p> <p>Three more mutations to watch:</p> <ul> <li><strong>Title and description edits</strong> on existing GUIDs, usually SEO tuning or a correction.</li> <li><strong>Enclosure URL changes</strong> on an unchanged GUID, which normally means a re-upload with a different ad load rather than new content.</li> <li><strong><code><itunes:new-feed-url></code> appearing at channel level</strong>, the standard signal that the show has migrated hosts. Follow it once and update your canonical URL, or your record goes stale within a month.</li> </ul> <p>Rate limiting discipline applies throughout. The <a href="https://www.sparkproxy.io/blog/guide-on-ethical-scraping-and-rate-limiting/">ethical scraping and rate limiting guide</a> covers the general shape. For feeds specifically, cap concurrency per host domain rather than globally, since a handful of large hosting providers serve a very long tail of shows.</p></div><!--[--><!--]--></section><!----><!--]--><!--[--><section id="collecting-podcast-data-with-the-sparkproxy-scraping-api" class="scroll-mt-28"><span class="mt-12 block h-1 w-10 rounded-full bg-brand" aria-hidden="true"></span><h2 class="group/h relative font-extrabold text-navy mb-4 mt-3 text-2xl md:text-[1.7rem]"><a href="#collecting-podcast-data-with-the-sparkproxy-scraping-api" class="absolute -left-6 top-1 hidden text-brand opacity-0 transition-opacity group-hover/h:opacity-100 lg:inline" aria-hidden="true">#</a> Collecting Podcast Data With the SparkProxy Scraping API</h2><!----><div class="blog-content"><p>Raw XML feeds usually need nothing more than a rotating pool and a sane User-Agent. The chart and storefront surfaces are where a managed layer pays for itself, because they are JavaScript-rendered and country-sensitive. Parameters below come from the <a href="https://www.sparkproxy.io/docs/scraping-api/">SparkProxy Scraping API docs</a>.</p> <p>A plain feed fetch through a rotating exit, no rendering needed:</p> <pre><code class="language-bash">curl -X GET "https://scrape.sparkproxy.io/api/v1?url=https://feeds.sparkproxy.io/show.xml&render_js=false" \ -H "X-API-Key: sk-xxxxxxxxxxxxxxxx" </code></pre> <p>Feed polling in Python, keeping the conditional-request logic intact:</p> <pre><code class="language-python">import requests API = "https://scrape.sparkproxy.io/api/v1" API_KEY = "sk-xxxxxxxxxxxxxxxx" def fetch_feed(feed_url, country=None): params = {"url": feed_url, "render_js": "false", "format": "json"} if country: params["country_code"] = country # ISO alpha-2: US, GB, DE r = requests.get(API, params=params, headers={"X-API-Key": API_KEY}, timeout=90) data = r.json() return data["status_code"], data["body"], data["credits_used"] </code></pre> <p>Pulling the Apple chart JSON for one storefront:</p> <pre><code class="language-python">def apple_chart(storefront: str, limit: int = 100): url = (f"https://rss.marketingtools.apple.com/api/v2/{storefront}" f"/podcasts/top/{limit}/podcasts.json") r = requests.get(API, params={"url": url, "format": "json"}, headers={"X-API-Key": API_KEY}, timeout=60) return r.json()["body"] </code></pre> <p>Checking how a show's storefront page renders to a listener in Japan, which needs a real browser and a local exit:</p> <pre><code class="language-bash">curl -X GET "https://scrape.sparkproxy.io/api/v1?url=https://podcasts.apple.com/jp/podcast/id1200361736&country_code=JP&render_js=true&wait_for=3000&format=html" \ -H "X-API-Key: sk-xxxxxxxxxxxxxxxx" </code></pre> <p>For storefronts that block datacenter ranges, route through the residential tier and pin a session so a multi-step check stays on one IP:</p> <pre><code class="language-python">params = { "url": "https://podcastcharts.byspotify.com/", "country_code": "BR", "render_js": "true", "premium_proxy": "true", "session_id": "spotify-charts-br", "wait_for": "4000", "format": "html", } r = requests.get(API, params=params, headers={"X-API-Key": API_KEY}, timeout=120) </code></pre> <p>Capturing dated visual evidence of a chart position, useful when a client disputes what ranked where:</p> <pre><code class="language-bash">curl -X GET "https://scrape.sparkproxy.io/api/v1?url=https://podcasts.apple.com/ie/charts&country_code=IE&render_js=true&format=screenshot" \ -H "X-API-Key: sk-xxxxxxxxxxxxxxxx" \ --output chart_ie_2026-08-18.png </code></pre> <p>Cost note: <code>render_js</code> and <code>premium_proxy</code> both cost more per request. Feed polls are the bulk of your volume and need neither, so keep them on the cheap path and reserve the expensive path for chart and storefront sweeps. The response returns <code>credits_used</code> per call, which makes it easy to spot when someone has accidentally left rendering enabled on the feed crawler.</p></div><!--[--><!--]--></section><!----><!--]--><!--[--><section id="the-honest-limit-you-cannot-scrape-downloads" class="scroll-mt-28"><span class="mt-12 block h-1 w-10 rounded-full bg-brand" aria-hidden="true"></span><h2 class="group/h relative font-extrabold text-navy mb-4 mt-3 text-2xl md:text-[1.7rem]"><a href="#the-honest-limit-you-cannot-scrape-downloads" class="absolute -left-6 top-1 hidden text-brand opacity-0 transition-opacity group-hover/h:opacity-100 lg:inline" aria-hidden="true">#</a> The Honest Limit: You Cannot Scrape Downloads</h2><!----><div class="blog-content"><p>This is the section that should decide whether you trust a podcast analytics vendor.</p> <p>A podcast download is counted server-side, at the moment a client requests bytes from the audio file. That request goes to the hosting provider's origin or CDN, and the count lives in their logs. There is no public endpoint, no rendered page, and no undocumented JSON that exposes it. The <a rel="noopener noreferrer" target="_blank" href="https://iabtechlab.com/standards/podcast-measurement-guidelines/">IAB Tech Lab Podcast Measurement Technical Guidelines</a> define what counts as a download at all, including the byte threshold served and the filtering of known bots, and compliance is certified per hosting platform. Nothing in that process touches a surface a crawler can reach.</p> <p>The same applies to platform-side listening. Apple Podcasts Connect and Spotify for Creators report plays, followers, completion and drop-off only to the verified owner of the show, behind authentication. Scraping those dashboards would require credentials you do not have, and using someone else's would be unauthorised access, not data collection.</p> <p>So what can you legitimately say from public data?</p> <div class="table-wrap"><table><thead><tr><th>Public signal</th><th>What it genuinely indicates</th><th>What it does not indicate</th></tr></thead><tbody><tr><td>Chart rank in a storefront</td><td>Recent momentum in follows and listens, relative to that market</td><td>Audience size, or comparability across countries</td></tr><tr><td>Chart persistence over weeks</td><td>Sustained engagement rather than a launch spike</td><td>Any absolute number</td></tr><tr><td>Publishing cadence and gaps</td><td>Production health, resourcing, likely hiatus</td><td>Listenership trend</td></tr><tr><td>Review and rating counts by storefront</td><td>Rough engaged-audience floor and geographic spread</td><td>Downloads. Review rates vary hugely by genre</td></tr><tr><td>Ad load, derived from episode duration drift</td><td>Monetisation intensity</td><td>Revenue</td></tr><tr><td>`podcast:funding` and sponsor mentions</td><td>Business model and active advertisers</td><td>Deal value</td></tr></tbody></table></div> <p>Every serious estimate of podcast audience size is a model fitted on these signals against a small set of shows whose real numbers are known through a partnership or a public disclosure. That is legitimate work, and it is also inference. Label it as an estimate and give it a confidence interval. If a dataset presents a download figure for a show it has no relationship with, and no interval on the number, the number was modelled, whatever the marketing says.</p> <p>Saying this plainly is not a weakness in your product. It is the thing that makes the rest of your numbers believable.</p></div><!--[--><!--]--></section><!----><!--]--><!--[--><section id="audio-transcripts-and-rights" class="scroll-mt-28"><span class="mt-12 block h-1 w-10 rounded-full bg-brand" aria-hidden="true"></span><h2 class="group/h relative font-extrabold text-navy mb-4 mt-3 text-2xl md:text-[1.7rem]"><a href="#audio-transcripts-and-rights" class="absolute -left-6 top-1 hidden text-brand opacity-0 transition-opacity group-hover/h:opacity-100 lg:inline" aria-hidden="true">#</a> Audio, Transcripts and Rights</h2><!----><div class="blog-content"><p>The boundary here is clean, and staying inside it costs you nothing analytically.</p> <p><strong>Do not download the audio.</strong> The <code><enclosure></code> URL is public, but the audio file is a copyrighted work, and pulling it at scale also inflates the publisher's download counts and their bandwidth bill. You would be corrupting the exact metric everyone else depends on. Read the metadata, skip the media.</p> <p><strong>Transcripts carry their own rights questions.</strong> A <code>podcast:transcript</code> tag links a file the publisher chose to make available, typically WebVTT under the <a rel="noopener noreferrer" target="_blank" href="https://www.w3.org/TR/webvtt1/">W3C WebVTT specification</a> or SRT. Published deliberately for accessibility is not the same as licensed for redistribution. The transcript is a derivative of a copyrighted work, so republishing it wholesale, or training on a corpus of them, is a licensing decision rather than a technical one. Reading a transcript to extract topics, entities or sponsor mentions for analysis is a very different act from serving that transcript as your own content.</p> <p><strong>Generating your own transcripts is worse, not better.</strong> Running speech-to-text over the audio means you first made a copy of the audio. The output is still derived from the original work.</p> <p><strong>Respect the mechanical signals.</strong> Check <code>robots.txt</code> on the host serving the feed, honour <code>podcast:locked</code> when the owner has blocked automated migration, and send a User-Agent with a contact URL so anyone who wants to talk to you can. The same courtesy that applies to <a href="https://www.sparkproxy.io/blog/using-proxies-for-social-media-monitoring/">social media monitoring</a> applies here, with the added point that podcast publishers are individually much smaller and much more likely to notice you.</p> <p>None of this stops you building a strong product. Feed metadata, Podcasting 2.0 tags, per-storefront chart history and catalogue change detection give you a picture of the medium that no single platform dashboard offers. The constraint is only on audio, and on claiming private numbers you do not have.</p></div><!--[--><!--]--></section><!----><!--]--><!--]--><div class="mt-14"><h2 class="mb-5 text-2xl font-extrabold text-navy">Frequently asked questions</h2><div class="blog-content faq-block"><div class="faq-accordion"> <h2 class="faq-heading">Frequently Asked Questions</h2> <div class="faq-item open"> <button class="faq-question" aria-expanded="true">Do I need proxies for podcast analytics and RSS feed scraping?<span class="faq-icon" aria-hidden="true"></span></button> <div class="faq-answer"> <p>For a few hundred feeds, no. Once you are polling thousands of feeds concentrated on a handful of large hosting providers, per-IP throttling and CDN bot rules make a rotating pool necessary, and conditional requests matter more than raw IP count. Chart collection is the part that genuinely needs distributed IPs.</p> </div> </div> <div class="faq-item"> <button class="faq-question" aria-expanded="false">Why do podcast chart rankings differ by country?<span class="faq-icon" aria-hidden="true"></span></button> <div class="faq-answer"> <p>Each Apple storefront and each Spotify country chart is computed only from activity in that market. A show with a concentrated regional audience can rank top ten in one storefront and be absent everywhere else, so podcast chart rankings by country are separate datasets, not variations of one list.</p> </div> </div> <div class="faq-item"> <button class="faq-question" aria-expanded="false">Can you scrape podcast download numbers?<span class="faq-icon" aria-hidden="true"></span></button> <div class="faq-answer"> <p>No. Downloads are counted server-side by the hosting provider under the IAB Tech Lab measurement guidelines, and listener data sits behind owner authentication in Apple Podcasts Connect and Spotify for Creators. Any public download figure is a model fitted on chart, review and cadence signals.</p> </div> </div> <div class="faq-item"> <button class="faq-question" aria-expanded="false">What is the Podcasting 2.0 namespace and is it worth parsing?<span class="faq-icon" aria-hidden="true"></span></button> <div class="faq-answer"> <p>It is a community-maintained XML extension adding transcripts, chapters, funding links, named people and a stable <code>podcast:guid</code>. Adoption is partial, but it sits in feeds you fetch anyway, so parsing it costs nothing and <code>podcast:guid</code> alone fixes cross-platform deduplication.</p> </div> </div> <div class="faq-item"> <button class="faq-question" aria-expanded="false">How do I detect when a podcast publishes a new episode?<span class="faq-icon" aria-hidden="true"></span></button> <div class="faq-answer"> <p>Poll with <code>If-None-Match</code> and <code>If-Modified-Since</code> so unchanged feeds return a 304, subscribe to WebSub hubs or Podping where hosts support them, and on any change compare episode GUID sets to find additions and removals rather than diffing the XML.</p> </div> </div> <div class="faq-item"> <button class="faq-question" aria-expanded="false">Is it legal to collect podcast data from public feeds?<span class="faq-icon" aria-hidden="true"></span></button> <div class="faq-answer"> <p>Reading public RSS metadata and public chart pages is ordinary collection of published information. Downloading the copyrighted audio, redistributing transcripts, or accessing an owner's private analytics dashboard are separate acts with separate legal exposure, so keep your pipeline to metadata.</p> </div> </div> </div></div></div><div class="mt-12 flex flex-col gap-6 border-t border-gray-100 pt-8"><div class="flex flex-wrap justify-center gap-2"><!--[--><a href="https://www.sparkproxy.io/blog/tag/proxies-for-podcast-analytics" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#proxies for podcast analytics</a><a href="https://www.sparkproxy.io/blog/tag/podcast-rss-feed-scraping" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#podcast rss feed scraping</a><a href="https://www.sparkproxy.io/blog/tag/podcast-chart-rankings-by-country" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#podcast chart rankings by country</a><a href="https://www.sparkproxy.io/blog/tag/podcasting-20-namespace" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#podcasting 2.0 namespace</a><a href="https://www.sparkproxy.io/blog/tag/podcast-data-collection" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#podcast data collection</a><a href="https://www.sparkproxy.io/blog/tag/media-monitoring" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#media monitoring</a><a href="https://www.sparkproxy.io/blog/tag/residential-proxies" rel="noopener noreferrer" class="rounded-full bg-gray-100 px-3 py-1 text-sm text-gray-600 transition hover:bg-brand hover:text-white">#residential 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Use the code at checkout.</p><p class="mt-2 text-xs font-semibold uppercase tracking-wide text-white/60" data-v-5ff897cc>Save up to 15% more on quarterly, half-yearly and yearly plans</p></div><div class="flex shrink-0 items-center gap-4" data-v-5ff897cc><div class="hidden rounded-xl border border-dashed border-white/45 bg-white/10 px-5 py-2.5 text-center sm:block" data-v-5ff897cc><span class="block text-[10px] font-bold uppercase tracking-widest text-white/70" data-v-5ff897cc>Code</span><span class="text-xl font-extrabold tracking-[0.22em]" data-v-5ff897cc>SPARK20</span></div><a href="/#pricing" class="group inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-xl bg-white px-7 py-3.5 text-sm font-extrabold text-brand shadow-lg transition hover:-translate-y-0.5 hover:shadow-xl" data-v-5ff897cc>Claim Discount <svg class="h-4 w-4 transition-transform group-hover:translate-x-1" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5" aria-hidden="true" data-v-5ff897cc><path stroke-linecap="round" stroke-linejoin="round" d="M5 12h14M13 6l6 6-6 6" data-v-5ff897cc></path></svg></a></div></div></div><div class="mt-12"><div id="about-the-author" class="scroll-mt-28 rounded-2xl bg-navy p-7 text-white shadow-lg shadow-navy/25 sm:p-8"><h2 class="mb-3 flex items-center gap-2.5 text-xl font-extrabold text-white"><span class="inline-block h-5 w-1 rounded-full bg-brand"></span> About the Author</h2><div class="blog-content author-bio"><p>The <strong>SparkProxy Technical Team</strong> builds and operates the infrastructure behind SparkProxy's datacenter proxies, residential proxies, and the SparkProxy Scraping API. We work daily with customers running geo-distributed collection across storefronts, feeds and public APIs, and we write from what those pipelines actually do in production. Full API parameters and pricing are in the <a href="https://www.sparkproxy.io/docs/scraping-api/">Scraping API documentation</a>. Questions about a specific collection architecture: support@sparkproxy.io.</p></div></div></div></div><aside class="hidden lg:block"><div class="sidebar-scroll sticky top-24 max-h-[calc(100vh-7rem)] overflow-y-auto overscroll-contain pr-1"><nav aria-label="On this page" class="rounded-2xl bg-brand p-6 text-white shadow-lg shadow-brand/25 mb-6"><p class="mb-4 flex items-center gap-2.5 border-b border-white/20 pb-3 text-sm font-extrabold uppercase tracking-widest"><svg class="h-4 w-4 shrink-0" viewBox="0 0 512 512" fill="currentColor" aria-hidden="true"><path d="M64 144a48 48 0 1 0 0-96 48 48 0 1 0 0 96zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM64 464a48 48 0 1 0 0-96 48 48 0 1 0 0 96zm48-208a48 48 0 1 0 -96 0 48 48 0 1 0 96 0z"></path></svg> On this page </p><ul class="space-y-0.5 border-l border-white/25"><!--[--><li><a href="#what-podcast-data-actually-exists-in-public" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">What Podcast Data Actually Exists in Public</a></li><li><a href="#feed-discovery-finding-the-rss-behind-a-show" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">Feed Discovery: Finding the RSS Behind a Show</a></li><li><a href="#parsing-the-feed-itunes-tags-and-the-real-structure" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">Parsing the Feed: iTunes Tags and the Real Structure</a></li><li><a href="#the-podcasting-20-namespace" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">The Podcasting 2.0 Namespace</a></li><li><a href="#why-chart-position-must-be-collected-per-country" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">Why Chart Position Must Be Collected Per Country</a></li><li><a href="#episode-and-catalogue-change-detection" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">Episode and Catalogue Change Detection</a></li><li><a href="#collecting-podcast-data-with-the-sparkproxy-scraping-api" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">Collecting Podcast Data With the SparkProxy Scraping API</a></li><li><a href="#the-honest-limit-you-cannot-scrape-downloads" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">The Honest Limit: You Cannot Scrape Downloads</a></li><li><a href="#audio-transcripts-and-rights" class="pl-3 text-sm font-semibold border-transparent text-white/65 hover:text-white -ml-px block border-l-2 py-1 leading-snug transition-colors">Audio, Transcripts and Rights</a></li><!--]--></ul></nav><div class="relative overflow-hidden rounded-2xl bg-gradient-to-br from-[#e11d3a] via-brand to-[#8f1428] p-5 text-white shadow-xl shadow-brand/30 ring-1 ring-white/10 mb-6" data-v-5ff897cc><div class="pointer-events-none absolute -right-10 -top-12 h-32 w-32 rounded-full bg-white/15 blur-2xl" aria-hidden="true" data-v-5ff897cc></div><span class="promo-shine pointer-events-none absolute inset-y-0 left-0 w-1/3 bg-gradient-to-r from-transparent via-white/25 to-transparent" aria-hidden="true" data-v-5ff897cc></span><div class="relative" data-v-5ff897cc><span class="mb-2.5 inline-flex items-center gap-1.5 rounded-full bg-white/15 px-2.5 py-1 text-[10px] font-extrabold uppercase tracking-widest ring-1 ring-white/25" data-v-5ff897cc><svg class="h-3 w-3" viewBox="0 0 384 512" fill="currentColor" aria-hidden="true" data-v-5ff897cc><path d="M0 256L28.5 28c2-16 15.6-28 31.8-28H228.9c15 0 27.1 12.1 27.1 27.1c0 3.2-.6 6.5-1.7 9.5L208 160H347.3c20.2 0 36.7 16.4 36.7 36.7c0 7.4-2.2 14.6-6.4 20.7l-192.2 281c-5.9 8.6-15.6 13.6-25.9 13.6h-2.9c-15.7 0-28.5-12.8-28.5-28.5c0-2.3 .3-4.6 .9-6.9L176 288H32c-17.7 0-32-14.3-32-32z" data-v-5ff897cc></path></svg> Free trial</span><p class="text-lg font-extrabold leading-snug" data-v-5ff897cc>Try Datacentre proxies</p><p class="mt-1.5 text-sm leading-relaxed text-white/85" data-v-5ff897cc>250 threads, zero cost, instant access.</p><!----><a href="/#pricing" class="group mt-4 flex items-center justify-center gap-2 rounded-lg bg-white px-4 py-2.5 text-sm font-extrabold text-brand shadow-md transition hover:-translate-y-0.5 hover:shadow-lg" data-v-5ff897cc>Start Free Trial <svg class="h-4 w-4 transition-transform group-hover:translate-x-1" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5" aria-hidden="true" data-v-5ff897cc><path stroke-linecap="round" stroke-linejoin="round" d="M5 12h14M13 6l6 6-6 6" data-v-5ff897cc></path></svg></a><!----></div></div><div class="columns-1 gap-8 md:columns-2 lg:columns-1 [&>section]:mb-8 [&>section]:break-inside-avoid [&>section:last-child]:mb-0"><section><h3 class="mb-4 flex items-center gap-2 border-b border-gray-100 pb-2 text-xs font-extrabold uppercase tracking-widest text-navy"><span class="inline-block h-4 w-1 rounded-full bg-brand"></span> Popular Posts </h3><ul class="space-y-4"><!--[--><li><a href="https://www.sparkproxy.io/blog/top-12-antidetect-browsers-best-tools-for-privacy-multi-account-management" rel="noopener noreferrer" class="group flex gap-3"><div class="h-14 w-16 shrink-0 overflow-hidden rounded-lg bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202609/image_140x98_6a966a19c6b8c.webp" alt="Best Antidetect Browsers: Top 12 Compared for 2026" width="64" height="56" loading="lazy" decoding="async" class="h-full w-full object-cover"></div><div class="min-w-0"><h4 class="line-clamp-2 text-sm font-bold leading-snug text-navy transition-colors group-hover:text-brand">Best Antidetect Browsers: Top 12 Compared for 2026</h4><time class="text-xs text-gray-400" datetime="2026-05-17T22:53:20+05:30">May 17, 2026</time></div></a></li><li><a href="https://www.sparkproxy.io/blog/what-is-proxy-server" rel="noopener noreferrer" class="group flex gap-3"><div class="h-14 w-16 shrink-0 overflow-hidden rounded-lg bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202605/image_140x98_6a06ac2b0ceb5.webp" alt="What Is a Proxy Server? Meaning & How It Works (2026)" width="64" height="56" loading="lazy" decoding="async" class="h-full w-full object-cover"></div><div class="min-w-0"><h4 class="line-clamp-2 text-sm font-bold leading-snug text-navy transition-colors group-hover:text-brand">What Is a Proxy Server? Meaning & How It Works (2026)</h4><time class="text-xs text-gray-400" datetime="2026-05-15T10:46:54+05:30">May 15, 2026</time></div></a></li><li><a href="https://www.sparkproxy.io/blog/what-is-an-anonymous-proxy" rel="noopener noreferrer" class="group flex gap-3"><div class="h-14 w-16 shrink-0 overflow-hidden rounded-lg bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202606/image_140x98_6a1e7733210b5.webp" alt="Anonymous Proxy: What It Is, How It Works, and Its Limits" width="64" height="56" loading="lazy" decoding="async" class="h-full w-full object-cover"></div><div class="min-w-0"><h4 class="line-clamp-2 text-sm font-bold leading-snug text-navy transition-colors group-hover:text-brand">Anonymous Proxy: What It Is, How It Works, and Its Limits</h4><time class="text-xs text-gray-400" datetime="2026-06-08T13:28:07+05:30">Jun 8, 2026</time></div></a></li><li><a href="https://www.sparkproxy.io/blog/guide-on-ethical-scraping-and-rate-limiting" rel="noopener noreferrer" class="group flex gap-3"><div class="h-14 w-16 shrink-0 overflow-hidden rounded-lg bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202605/image_140x98_69fae601a240a.webp" alt="Ethical Web Scraping: Rate Limiting & robots.txt Guide (2026)" width="64" height="56" loading="lazy" decoding="async" class="h-full w-full object-cover"></div><div class="min-w-0"><h4 class="line-clamp-2 text-sm font-bold leading-snug text-navy transition-colors group-hover:text-brand">Ethical Web Scraping: Rate Limiting & robots.txt Guide (2026)</h4><time class="text-xs text-gray-400" datetime="2026-05-06T12:18:22+05:30">May 6, 2026</time></div></a></li><li><a href="https://www.sparkproxy.io/blog/proxy-ports-explained-80-443-8080-and-more" rel="noopener noreferrer" class="group flex gap-3"><div class="h-14 w-16 shrink-0 overflow-hidden rounded-lg bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202606/image_140x98_6a37d92fd9edc.webp" alt="Proxy Ports Explained: 80, 443, 8080 & Which to Use" width="64" height="56" loading="lazy" decoding="async" class="h-full w-full object-cover"></div><div class="min-w-0"><h4 class="line-clamp-2 text-sm font-bold leading-snug text-navy transition-colors group-hover:text-brand">Proxy Ports Explained: 80, 443, 8080 & Which to Use</h4><time class="text-xs text-gray-400" datetime="2026-06-21T17:56:45+05:30">Jun 21, 2026</time></div></a></li><!--]--></ul></section><section class="rounded-2xl bg-brand p-5 text-white shadow-lg shadow-brand/20"><h3 class="mb-4 flex items-center gap-2 border-b border-white/20 pb-2 text-xs font-extrabold uppercase tracking-widest text-white"><span class="inline-block h-4 w-1 rounded-full bg-white"></span> Follow Us </h3><div class="flex gap-3"><!--[--><a href="https://www.facebook.com/sparkproxy.io" target="_blank" rel="noopener noreferrer" aria-label="Facebook" title="Facebook" class="flex h-10 w-10 items-center justify-center rounded-lg bg-white text-navy shadow-sm transition-colors hover:bg-navy 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What Buyers Need to Know</h4><time class="text-xs text-gray-400" datetime="2026-09-08T21:00:01+05:30">Sep 8, 2026</time></div></a></li><!--]--></ul></section><section class="rounded-2xl bg-brand p-5 text-white shadow-lg shadow-brand/20"><h3 class="mb-4 flex items-center gap-2 border-b border-white/20 pb-2 text-xs font-extrabold uppercase tracking-widest text-white"><span class="inline-block h-4 w-1 rounded-full bg-white"></span> Popular Tags </h3><div class="flex flex-wrap gap-2"><!--[--><a href="https://www.sparkproxy.io/blog/tag/web-scraping" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#web scraping</a><a href="https://www.sparkproxy.io/blog/tag/residential-proxies" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#residential proxies</a><a href="https://www.sparkproxy.io/blog/tag/datacenter-proxies" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#datacenter proxies</a><a href="https://www.sparkproxy.io/blog/tag/scraping-api" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#scraping api</a><a href="https://www.sparkproxy.io/blog/tag/multi-account-management" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#multi-account management</a><a href="https://www.sparkproxy.io/blog/tag/antidetect-browser" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#antidetect browser</a><a href="https://www.sparkproxy.io/blog/tag/web-scraping-api" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#web scraping api</a><a href="https://www.sparkproxy.io/blog/tag/proxy-comparison" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#proxy comparison</a><a href="https://www.sparkproxy.io/blog/tag/browser-fingerprinting" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#browser fingerprinting</a><a href="https://www.sparkproxy.io/blog/tag/proxy-pricing" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#proxy pricing</a><a href="https://www.sparkproxy.io/blog/tag/proxy-types" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#proxy types</a><a href="https://www.sparkproxy.io/blog/tag/mobile-proxies" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#mobile proxies</a><a href="https://www.sparkproxy.io/blog/tag/gologin" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#gologin</a><a href="https://www.sparkproxy.io/blog/tag/proxy" rel="noopener noreferrer" class="rounded-full bg-white/15 px-3 py-1 text-xs text-white transition hover:bg-white hover:text-brand">#proxy</a><!--]--></div></section></div></div></aside></div></div><section class="section border-t border-gray-100 bg-gray-50"><div class="mx-auto max-w-[1440px] px-4 sm:px-6 md:px-8 lg:px-12"><div class="mb-10 text-center"><span class="mb-3 inline-block rounded-full bg-brand-light px-3 py-1 text-xs font-bold uppercase tracking-widest text-brand">Keep reading</span><h2 class="text-2xl font-bold text-navy md:text-3xl">Related articles</h2></div><div class="grid grid-cols-1 gap-6 sm:grid-cols-2 lg:grid-cols-3"><!--[--><article class="group flex flex-col overflow-hidden rounded-2xl border border-gray-200 bg-white transition-all duration-200 hover:-translate-y-0.5 hover:shadow-md"><a href="https://www.sparkproxy.io/blog/best-proxies-for-ai-agents" rel="noopener noreferrer" class="block aspect-[16/9] overflow-hidden bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202609/image_870x_6a9ff75f251e4.webp" alt="Best Proxies for AI Agents and Autonomous Browsing" width="640" height="360" loading="lazy" decoding="async" class="h-full w-full object-cover"></a><div class="flex flex-1 flex-col p-5"><h3 class="mb-2 line-clamp-2 min-h-[3.25rem] text-lg font-bold leading-snug text-navy"><a href="https://www.sparkproxy.io/blog/best-proxies-for-ai-agents" rel="noopener noreferrer" class="transition-colors group-hover:text-brand">Best Proxies for AI Agents and Autonomous Browsing</a></h3><p class="mb-4 line-clamp-2 min-h-[2.5rem] text-sm leading-relaxed text-gray-500">Which proxies for AI agents to buy: datacenter, ISP or residential, how to size threads for browser agents, and what a runaway agent loop costs you.</p><div class="mt-auto flex flex-wrap items-center gap-2 text-xs text-gray-400"><span class="font-semibold text-gray-500">SparkProxy</span><span>·</span><time datetime="2026-09-08T20:00:02+05:30">Sep 8, 2026</time><span class="ml-auto rounded-full px-2 py-0.5 text-[10px] font-bold uppercase tracking-wide text-white" style="background-color:#c51e39;">Use Cases</span></div></div></article><article class="group flex flex-col overflow-hidden rounded-2xl border border-gray-200 bg-white transition-all duration-200 hover:-translate-y-0.5 hover:shadow-md"><a href="https://www.sparkproxy.io/blog/proxies-for-distributed-load-testing" rel="noopener noreferrer" class="block aspect-[16/9] overflow-hidden bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202609/image_870x_6a9679f619783.webp" alt="Using Proxies for Distributed Load Testing" width="640" height="360" loading="lazy" decoding="async" class="h-full w-full object-cover"></a><div class="flex flex-1 flex-col p-5"><h3 class="mb-2 line-clamp-2 min-h-[3.25rem] text-lg font-bold leading-snug text-navy"><a href="https://www.sparkproxy.io/blog/proxies-for-distributed-load-testing" rel="noopener noreferrer" class="transition-colors group-hover:text-brand">Using Proxies for Distributed Load Testing</a></h3><p class="mb-4 line-clamp-2 min-h-[2.5rem] text-sm leading-relaxed text-gray-500">Proxies for distributed load testing: why single-IP runs mislead, how to size concurrency against plan threads, and k6, JMeter, Locust and Gatling setup.</p><div class="mt-auto flex flex-wrap items-center gap-2 text-xs text-gray-400"><span class="font-semibold text-gray-500">SparkProxy</span><span>·</span><time datetime="2026-09-07T10:00:01+05:30">Sep 7, 2026</time><span class="ml-auto rounded-full px-2 py-0.5 text-[10px] font-bold uppercase tracking-wide text-white" style="background-color:#c51e39;">Use Cases</span></div></div></article><article class="group flex flex-col overflow-hidden rounded-2xl border border-gray-200 bg-white transition-all duration-200 hover:-translate-y-0.5 hover:shadow-md"><a href="https://www.sparkproxy.io/blog/proxies-for-web3-and-nft-marketplace-data" rel="noopener noreferrer" class="block aspect-[16/9] overflow-hidden bg-navy/5"><img src="https://blog.cms.sparkproxy.io/uploads/images/202608/image_870x_6a958c96eda0a.webp" alt="Proxies for Web3 Data and NFT Marketplace Feeds" width="640" height="360" loading="lazy" decoding="async" class="h-full w-full object-cover"></a><div class="flex flex-1 flex-col p-5"><h3 class="mb-2 line-clamp-2 min-h-[3.25rem] text-lg font-bold leading-snug text-navy"><a href="https://www.sparkproxy.io/blog/proxies-for-web3-and-nft-marketplace-data" rel="noopener noreferrer" class="transition-colors group-hover:text-brand">Proxies for Web3 Data and NFT Marketplace Feeds</a></h3><p class="mb-4 line-clamp-2 min-h-[2.5rem] text-sm leading-relaxed text-gray-500">Proxies for Web3 data: where they fix IPFS gateway and marketplace throttling, where an API key makes them useless, and how to collect NFT floor and trait data.</p><div class="mt-auto flex flex-wrap items-center gap-2 text-xs text-gray-400"><span class="font-semibold text-gray-500">SparkProxy</span><span>·</span><time datetime="2026-09-05T10:00:01+05:30">Sep 5, 2026</time><span class="ml-auto rounded-full px-2 py-0.5 text-[10px] font-bold uppercase tracking-wide text-white" style="background-color:#c51e39;">Use Cases</span></div></div></article><!--]--></div></div></section></article></div><!--]--></main><button type="button" aria-label="Back to top" class="fixed bottom-24 right-5 z-40 flex h-11 w-11 items-center justify-center rounded-full bg-brand text-white shadow-lg shadow-brand/30 transition-colors hover:bg-brand-dark focus:outline-none focus-visible:ring-2 focus-visible:ring-brand focus-visible:ring-offset-2 sm:h-12 sm:w-12" style="display:none;" 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India<br><a href="mailto:support@sparkproxy.io" class="text-red-300 transition-colors hover:text-red-200"> support@sparkproxy.io </a></address></div><div class="grid grid-cols-2 gap-8 md:grid-cols-4"><div><h3 class="mb-4 text-sm font-bold capitalize tracking-wider text-white">Quick Links</h3><ul class="space-y-2"><!--[--><li><a href="/free-premium-proxies/" class="text-sm text-white/60 transition-colors hover:text-brand">Free Premium Proxies</a></li><li><a href="/about/" class="text-sm text-white/60 transition-colors hover:text-brand">About Us</a></li><li><a href="/blog/" class="text-sm text-white/60 transition-colors hover:text-brand">Blog</a></li><li><a href="/#countries" class="text-sm text-white/60 transition-colors hover:text-brand">Locations</a></li><li><a href="/#pricing" class="text-sm text-white/60 transition-colors hover:text-brand">Pricing</a></li><!--]--></ul></div><div><h3 class="mb-4 text-sm font-bold capitalize tracking-wider text-white">Scraping API</h3><ul class="space-y-2"><li><a href="/scraping-api/" class="text-sm text-white/60 transition-colors hover:text-brand">Overview</a></li><li><a href="/docs/scraping-api/" class="text-sm text-white/60 transition-colors hover:text-brand">API Reference</a></li><li><a href="/docs/scraping-api/" class="text-sm text-white/60 transition-colors hover:text-brand">Documentation</a></li><li><a href="/docs/files/" class="text-sm text-white/60 transition-colors hover:text-brand">Async Results</a></li><li><a href="/scraping-api/#pricing" class="text-sm text-white/60 transition-colors hover:text-brand">Pricing</a></li></ul></div><div><h3 class="mb-4 text-sm font-bold capitalize tracking-wider text-white">Proxies</h3><ul class="space-y-2 text-sm text-white/60"><li>USA Rotating Proxies</li><li>Worldwide Rotating Proxies</li></ul><a href="https://app.sparkproxy.io/" target="_blank" rel="noopener" class="mt-4 inline-flex items-center gap-2 rounded-xl bg-brand px-4 py-2 text-sm font-bold text-white transition-all hover:bg-brand-dark hover:shadow-brand"> Sign In </a></div><div><h3 class="mb-4 text-sm font-bold capitalize tracking-wider text-white">Legal</h3><ul class="space-y-2"><!--[--><li><a href="/terms-of-use/" class="text-sm text-white/60 transition-colors hover:text-brand">Terms of Use</a></li><li><a href="/privacy-policy/" class="text-sm text-white/60 transition-colors hover:text-brand">Privacy Policy</a></li><li><a href="/refund-policy/" class="text-sm text-white/60 transition-colors hover:text-brand">Refund Policy</a></li><li><a href="/disclaimer/" class="text-sm text-white/60 transition-colors hover:text-brand">Disclaimer</a></li><li><a href="/cookie-policy/" class="text-sm text-white/60 transition-colors hover:text-brand">Cookie Policy</a></li><!--]--></ul></div></div></div><div class="border-t border-white/10 pt-8 flex flex-col sm:flex-row items-center justify-between gap-3 text-sm text-white/60"><p>Copyright © 2026. All Rights Reserved To SparkProxy.</p></div></div></footer></div><!----><!--]--></div><div id="teleports"></div><script>window.__NUXT__={};window.__NUXT__.config={public:{},app:{baseURL:"/",buildId:"b6139185-9350-4722-b271-41cd7be96184",buildAssetsDir:"/_nuxt/",cdnURL:""}}</script> <script type="application/json" data-nuxt-data="nuxt-app" data-ssr="true" id="__NUXT_DATA__">[["ShallowReactive",1],{"data":2,"state":707,"once":715,"_errors":716,"serverRendered":5,"path":718},["ShallowReactive",3],{"$fJZCwKeXznEeNLZ6UK0eizgjCfc-5MJoMJQ5jE1xY3Nw":4,"$ftobFREwkbef1kEw4fzFEYg3CR0Z57HebcGPY6pmOlQk":202,"blog-sidebar":588},{"ok":5,"data":6},true,{"type":7,"slug":8,"post":9},"post","proxies-for-podcast-and-media-analytics",{"id":10,"slug":8,"url":11,"title":12,"summary":13,"image":14,"category":16,"author":21,"post_type":25,"views":26,"published_at":27,"updated_at":28,"content_html":29,"faq_html":30,"faq":31,"keywords":50,"optional_url":15,"is_feed_post":51,"reading_time":52,"tags":53,"related":82,"type_payload":132,"seo":201},294,"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fproxies-for-podcast-and-media-analytics","Proxies for Podcast Analytics and Media Data","How proxies for podcast analytics collect RSS feeds, Podcasting 2.0 tags and per-country chart rankings, and the download numbers you honestly cannot scrape.",{"big":15,"default":15,"slider":15,"mid":15,"small":15,"alt":12},"",{"name":17,"slug":18,"color":19,"url":20},"Use Cases","use-cases","#c51e39","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fuse-cases",{"name":22,"slug":23,"url":24},"SparkProxy","sparkproxy","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fprofile\u002Fsparkproxy","table_of_contents",0,"2026-08-28T12:00:01+05:30",null,"\u003Cp>\u003Cstrong>Proxies for podcast analytics\u003C\u002Fstrong> exist because podcast data is split down the middle: the RSS layer is genuinely open and machine-readable, while charts, rankings and availability sit inside per-country Apple and Spotify storefronts that return a different answer depending on where your request comes from.\u003C\u002Fp>\n\u003Cp>That split decides your whole architecture. Feeds you can parse with an XML library and a polite crawler. Charts you have to collect roughly 175 times, once per storefront, because a show sitting at #4 in Ireland may not chart at all in the United States. And the number everyone actually wants, downloads, is not on the public internet in any form.\u003C\u002Fp>\n\u003Cblockquote>\u003Cp>\u003Cstrong>Key Takeaways\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Podcast RSS is an open, documented format. The Podcasting 2.0 namespace adds transcripts, chapters, funding links and a stable \u003Ccode>podcast:guid\u003C\u002Fcode> that survives feed URL changes.\u003C\u002Fli>\n\u003Cli>Chart position is storefront-specific. You must collect per country, and Apple exposes a clean public JSON endpoint per storefront that most teams never find.\u003C\u002Fli>\n\u003Cli>Episode and catalogue change detection runs on conditional HTTP requests plus GUID set comparison, not on refetching millions of feeds every hour.\u003C\u002Fli>\n\u003Cli>Real download and listener counts live with the hosting provider and the platform. They cannot be scraped. Anything sold as a \"download estimate\" is an inference from public popularity signals.\u003C\u002Fli>\n\u003C\u002Ful>\u003C\u002Fblockquote>","\u003Cdiv class=\"faq-accordion\">\n \u003Ch2 class=\"faq-heading\">Frequently Asked Questions\u003C\u002Fh2>\n \u003Cdiv class=\"faq-item open\">\n \u003Cbutton class=\"faq-question\" aria-expanded=\"true\">Do I need proxies for podcast analytics and RSS feed scraping?\u003Cspan class=\"faq-icon\" aria-hidden=\"true\">\u003C\u002Fspan>\u003C\u002Fbutton>\n \u003Cdiv class=\"faq-answer\">\n \u003Cp>For a few hundred feeds, no. Once you are polling thousands of feeds concentrated on a handful of large hosting providers, per-IP throttling and CDN bot rules make a rotating pool necessary, and conditional requests matter more than raw IP count. Chart collection is the part that genuinely needs distributed IPs.\u003C\u002Fp>\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n \u003Cdiv class=\"faq-item\">\n \u003Cbutton class=\"faq-question\" aria-expanded=\"false\">Why do podcast chart rankings differ by country?\u003Cspan class=\"faq-icon\" aria-hidden=\"true\">\u003C\u002Fspan>\u003C\u002Fbutton>\n \u003Cdiv class=\"faq-answer\">\n \u003Cp>Each Apple storefront and each Spotify country chart is computed only from activity in that market. A show with a concentrated regional audience can rank top ten in one storefront and be absent everywhere else, so podcast chart rankings by country are separate datasets, not variations of one list.\u003C\u002Fp>\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n \u003Cdiv class=\"faq-item\">\n \u003Cbutton class=\"faq-question\" aria-expanded=\"false\">Can you scrape podcast download numbers?\u003Cspan class=\"faq-icon\" aria-hidden=\"true\">\u003C\u002Fspan>\u003C\u002Fbutton>\n \u003Cdiv class=\"faq-answer\">\n \u003Cp>No. Downloads are counted server-side by the hosting provider under the IAB Tech Lab measurement guidelines, and listener data sits behind owner authentication in Apple Podcasts Connect and Spotify for Creators. Any public download figure is a model fitted on chart, review and cadence signals.\u003C\u002Fp>\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n \u003Cdiv class=\"faq-item\">\n \u003Cbutton class=\"faq-question\" aria-expanded=\"false\">What is the Podcasting 2.0 namespace and is it worth parsing?\u003Cspan class=\"faq-icon\" aria-hidden=\"true\">\u003C\u002Fspan>\u003C\u002Fbutton>\n \u003Cdiv class=\"faq-answer\">\n \u003Cp>It is a community-maintained XML extension adding transcripts, chapters, funding links, named people and a stable \u003Ccode>podcast:guid\u003C\u002Fcode>. Adoption is partial, but it sits in feeds you fetch anyway, so parsing it costs nothing and \u003Ccode>podcast:guid\u003C\u002Fcode> alone fixes cross-platform deduplication.\u003C\u002Fp>\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n \u003Cdiv class=\"faq-item\">\n \u003Cbutton class=\"faq-question\" aria-expanded=\"false\">How do I detect when a podcast publishes a new episode?\u003Cspan class=\"faq-icon\" aria-hidden=\"true\">\u003C\u002Fspan>\u003C\u002Fbutton>\n \u003Cdiv class=\"faq-answer\">\n \u003Cp>Poll with \u003Ccode>If-None-Match\u003C\u002Fcode> and \u003Ccode>If-Modified-Since\u003C\u002Fcode> so unchanged feeds return a 304, subscribe to WebSub hubs or Podping where hosts support them, and on any change compare episode GUID sets to find additions and removals rather than diffing the XML.\u003C\u002Fp>\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n \u003Cdiv class=\"faq-item\">\n \u003Cbutton class=\"faq-question\" aria-expanded=\"false\">Is it legal to collect podcast data from public feeds?\u003Cspan class=\"faq-icon\" aria-hidden=\"true\">\u003C\u002Fspan>\u003C\u002Fbutton>\n \u003Cdiv class=\"faq-answer\">\n \u003Cp>Reading public RSS metadata and public chart pages is ordinary collection of published information. Downloading the copyrighted audio, redistributing transcripts, or accessing an owner's private analytics dashboard are separate acts with separate legal exposure, so keep your pipeline to metadata.\u003C\u002Fp>\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n\u003C\u002Fdiv>",[32,35,38,41,44,47],{"question":33,"answer":34},"Do I need proxies for podcast analytics and RSS feed scraping?","For a few hundred feeds, no. Once you are polling thousands of feeds concentrated on a handful of large hosting providers, per-IP throttling and CDN bot rules make a rotating pool necessary, and conditional requests matter more than raw IP count. Chart collection is the part that genuinely needs distributed IPs.",{"question":36,"answer":37},"Why do podcast chart rankings differ by country?","Each Apple storefront and each Spotify country chart is computed only from activity in that market. A show with a concentrated regional audience can rank top ten in one storefront and be absent everywhere else, so podcast chart rankings by country are separate datasets, not variations of one list.",{"question":39,"answer":40},"Can you scrape podcast download numbers?","No. Downloads are counted server-side by the hosting provider under the IAB Tech Lab measurement guidelines, and listener data sits behind owner authentication in Apple Podcasts Connect and Spotify for Creators. Any public download figure is a model fitted on chart, review and cadence signals.",{"question":42,"answer":43},"What is the Podcasting 2.0 namespace and is it worth parsing?","It is a community-maintained XML extension adding transcripts, chapters, funding links, named people and a stable podcast:guid. Adoption is partial, but it sits in feeds you fetch anyway, so parsing it costs nothing and podcast:guid alone fixes cross-platform deduplication.",{"question":45,"answer":46},"How do I detect when a podcast publishes a new episode?","Poll with If-None-Match and If-Modified-Since so unchanged feeds return a 304, subscribe to WebSub hubs or Podping where hosts support them, and on any change compare episode GUID sets to find additions and removals rather than diffing the XML.",{"question":48,"answer":49},"Is it legal to collect podcast data from public feeds?","Reading public RSS metadata and public chart pages is ordinary collection of published information. Downloading the copyrighted audio, redistributing transcripts, or accessing an owner's private analytics dashboard are separate acts with separate legal exposure, so keep your pipeline to metadata.","proxies for podcast analytics,podcast rss feed scraping,podcast chart rankings by country,podcasting 2.0 namespace,podcast data collection,media monitoring,residential proxies",false,21,[54,58,62,66,70,74,78],{"name":55,"slug":56,"url":57},"proxies for podcast analytics","proxies-for-podcast-analytics","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fproxies-for-podcast-analytics",{"name":59,"slug":60,"url":61},"podcast rss feed scraping","podcast-rss-feed-scraping","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fpodcast-rss-feed-scraping",{"name":63,"slug":64,"url":65},"podcast chart rankings by country","podcast-chart-rankings-by-country","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fpodcast-chart-rankings-by-country",{"name":67,"slug":68,"url":69},"podcasting 2.0 namespace","podcasting-20-namespace","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fpodcasting-20-namespace",{"name":71,"slug":72,"url":73},"podcast data collection","podcast-data-collection","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fpodcast-data-collection",{"name":75,"slug":76,"url":77},"media monitoring","media-monitoring","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fmedia-monitoring",{"name":79,"slug":80,"url":81},"residential proxies","residential-proxies","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Ftag\u002Fresidential-proxies",[83,99,115],{"id":84,"slug":85,"url":86,"title":87,"summary":88,"image":89,"category":95,"author":96,"post_type":25,"views":97,"published_at":98,"updated_at":28},376,"best-proxies-for-ai-agents","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fbest-proxies-for-ai-agents","Best Proxies for AI Agents and Autonomous Browsing","Which proxies for AI agents to buy: datacenter, ISP or residential, how to size threads for browser agents, and what a runaway agent loop costs you.",{"big":90,"default":91,"slider":92,"mid":93,"small":94,"alt":87},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_870x580_6a9ff75e97fbe.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_870x_6a9ff75f251e4.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_694x532_6a9ff75f8970e.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_430x256_6a9ff75fe824c.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_140x98_6a9ff760449e6.webp",{"name":17,"slug":18,"color":19,"url":20},{"name":22,"slug":23,"url":24},2,"2026-09-08T20:00:02+05:30",{"id":100,"slug":101,"url":102,"title":103,"summary":104,"image":105,"category":111,"author":112,"post_type":25,"views":113,"published_at":114,"updated_at":28},350,"proxies-for-distributed-load-testing","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fproxies-for-distributed-load-testing","Using Proxies for Distributed Load Testing","Proxies for distributed load testing: why single-IP runs mislead, how to size concurrency against plan threads, and k6, JMeter, Locust and Gatling setup.",{"big":106,"default":107,"slider":108,"mid":109,"small":110,"alt":103},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_870x580_6a9679f59c52f.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_870x_6a9679f619783.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_694x532_6a9679f675db0.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_430x256_6a9679f6cb49a.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202609\u002Fimage_140x98_6a9679f71affa.webp",{"name":17,"slug":18,"color":19,"url":20},{"name":22,"slug":23,"url":24},1,"2026-09-07T10:00:01+05:30",{"id":116,"slug":117,"url":118,"title":119,"summary":120,"image":121,"category":127,"author":128,"post_type":25,"views":129,"published_at":130,"updated_at":131},298,"proxies-for-web3-and-nft-marketplace-data","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fproxies-for-web3-and-nft-marketplace-data","Proxies for Web3 Data and NFT Marketplace Feeds","Proxies for Web3 data: where they fix IPFS gateway and marketplace throttling, where an API key makes them useless, and how to collect NFT floor and trait data.",{"big":122,"default":123,"slider":124,"mid":125,"small":126,"alt":119},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_870x580_6a958c9667cd6.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_870x_6a958c96eda0a.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_694x532_6a958c97592a1.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_430x256_6a958c97b5fa8.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_140x98_6a958c9812d48.webp",{"name":17,"slug":18,"color":19,"url":20},{"name":22,"slug":23,"url":24},5,"2026-09-05T10:00:01+05:30","2026-08-31T19:45:50+05:30",{"kind":25,"sections":133},[134,140,146,153,160,166,173,180,187,194],{"id":135,"title":136,"anchor":137,"content_html":138,"image":15,"image_alt":15,"item_order":113,"parent_link_num":26,"children":139},4441,"What Podcast Data Actually Exists in Public","what-podcast-data-actually-exists-in-public","\u003Cp>Sort the field into three tiers before writing any code, because each tier needs a different collection method and carries a different confidence level.\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Tier\u003C\u002Fth>\u003Cth>Examples\u003C\u002Fth>\u003Cth>Where it lives\u003C\u002Fth>\u003Cth>Collection method\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Open and authoritative\u003C\u002Ftd>\u003Ctd>Titles, descriptions, episode list, publish dates, durations, categories, transcript links, funding links\u003C\u002Ftd>\u003Ctd>The show's own RSS feed, served over plain HTTP\u003C\u002Ftd>\u003Ctd>Fetch and parse XML\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Public but platform-specific\u003C\u002Ftd>\u003Ctd>Chart rank, category rank, storefront availability, artwork, ratings and review counts\u003C\u002Ftd>\u003Ctd>Apple Podcasts and Spotify surfaces, one result set per country\u003C\u002Ftd>\u003Ctd>Per-storefront collection, geo-aware\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Private\u003C\u002Ftd>\u003Ctd>Downloads, unique listeners, completion rate, drop-off curve, demographics\u003C\u002Ftd>\u003Ctd>The hosting provider's servers and each platform's creator dashboard\u003C\u002Ftd>\u003Ctd>Not available. Full stop.\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Tier one is the reason podcasting stayed an open ecosystem while video consolidated. A podcast is, structurally, an RSS 2.0 document with an \u003Ccode>\u003Cenclosure>\u003C\u002Fcode> element pointing at an audio file. The \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fwww.rssboard.org\u002Frss-specification\">RSS 2.0 specification\u003C\u002Fa> has not meaningfully changed since 2003, and Apple's \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fpodcasters.apple.com\u002Fsupport\u002F823-podcast-requirements\">podcast feed requirements\u003C\u002Fa> layer an \u003Ccode>itunes:\u003C\u002Fcode> namespace on top of it rather than replacing it. Nobody needs an API key to read a feed. That is by design.\u003C\u002Fp>\n\u003Cp>Tier two is where proxies earn their place. Tier three is where honest analytics products draw a line that their competitors often blur, and we will get to that line.\u003C\u002Fp>",[],{"id":141,"title":142,"anchor":143,"content_html":144,"image":15,"image_alt":15,"item_order":97,"parent_link_num":26,"children":145},4442,"Feed Discovery: Finding the RSS Behind a Show","feed-discovery-finding-the-rss-behind-a-show","\u003Cp>You almost never start with a feed URL. You start with a show name, an Apple collection ID, or a Spotify URI, and you need to resolve it to the canonical RSS.\u003C\u002Fp>\n\u003Ch4 id=\"route-1-the-itunes-lookup-api\">Route 1: the iTunes Lookup API\u003C\u002Fh4>\n\u003Cp>Apple runs a free, unauthenticated JSON endpoint that returns \u003Ccode>feedUrl\u003C\u002Fcode> for any podcast collection ID. It is documented in Apple's \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fdeveloper.apple.com\u002Flibrary\u002Farchive\u002Fdocumentation\u002FAudioVideo\u002FConceptual\u002FiTuneSearchAPI\u002F\">iTunes Search API reference\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-bash\">curl -s "https:\u002F\u002Fitunes.apple.com\u002Flookup?id=1200361736&entity=podcast"\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>The response carries \u003Ccode>collectionName\u003C\u002Fcode>, \u003Ccode>artistName\u003C\u002Fcode>, \u003Ccode>genres\u003C\u002Fcode>, \u003Ccode>trackCount\u003C\u002Fcode> and, critically, \u003Ccode>feedUrl\u003C\u002Fcode>. Two things bite people here. Apple documents a soft limit of roughly 20 calls per minute per IP, and the results are storefront-sensitive: add \u003Ccode>&country=DE\u003C\u002Fcode> and a show unavailable in Germany returns \u003Ccode>resultCount: 0\u003C\u002Fcode>. That second behaviour is useful, since it doubles as an availability probe.\u003C\u002Fp>\n\u003Ch4 id=\"route-2-the-podcast-index-api\">Route 2: the Podcast Index API\u003C\u002Fh4>\n\u003Cp>The open \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fpodcastindex-org.github.io\u002Fdocs-api\u002F\">Podcast Index API\u003C\u002Fa> indexes several million feeds and supports lookup by Apple ID, feed URL, GUID or search term. Auth is an odd but simple scheme: a SHA-1 of your API key, secret and current Unix timestamp in the \u003Ccode>Authorization\u003C\u002Fcode> header.\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">import hashlib, time, requests\n\nKEY, SECRET = "your-key", "your-secret"\nnow = str(int(time.time()))\nauth = hashlib.sha1((KEY + SECRET + now).encode()).hexdigest()\n\nr = requests.get(\n "https:\u002F\u002Fapi.podcastindex.org\u002Fapi\u002F1.0\u002Fpodcasts\u002Fbyitunesid",\n params={"id": 1200361736},\n headers={\n "User-Agent": "SparkProxyResearch\u002F1.0",\n "X-Auth-Key": KEY,\n "X-Auth-Date": now,\n "Authorization": auth,\n },\n timeout=30,\n)\nfeed = r.json()["feed"]\nprint(feed["url"], feed["podcastGuid"])\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch4 id=\"route-3-the-show-s-own-website\">Route 3: the show's own website\u003C\u002Fh4>\n\u003Cp>Well-built podcast sites still advertise the feed in the document head:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-html\"><link rel="alternate" type="application\u002Frss+xml" title="Feed" href="https:\u002F\u002Ffeeds.sparkproxy.io\u002Fshow.xml">\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Worth checking, because it sometimes reveals the raw host feed behind a tracking-prefixed URL that Apple displays instead.\u003C\u002Fp>\n\u003Cp>Spotify is the awkward one. An \u003Ccode>open.spotify.com\u002Fshow\u002F...\u003C\u002Fcode> URI does not resolve to an RSS feed through any public API, because Spotify deliberately does not expose the underlying feed. Matching a Spotify show to its RSS means fuzzy matching on title plus publisher plus the first episode's publish date, and you should carry a confidence score on that join rather than pretending it is exact.\u003C\u002Fp>",[],{"id":147,"title":148,"anchor":149,"content_html":150,"image":15,"image_alt":15,"item_order":151,"parent_link_num":26,"children":152},4443,"Parsing the Feed: iTunes Tags and the Real Structure","parsing-the-feed-itunes-tags-and-the-real-structure","\u003Cp>A podcast feed is one \u003Ccode>\u003Cchannel>\u003C\u002Fcode> with N \u003Ccode>\u003Citem>\u003C\u002Fcode> elements. The fields that matter for analytics are a mix of core RSS and the \u003Ccode>itunes:\u003C\u002Fcode> namespace:\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Field\u003C\u002Fth>\u003Cth>Level\u003C\u002Fth>\u003Cth>Why it matters\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>`\u003Ctitle>`, `\u003Citunes:author>`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>Entity resolution across platforms\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003Citunes:type>`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>`episodic` or `serial`, changes how you read episode order\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003Citunes:category>`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>Apple's taxonomy, drives which chart the show competes in\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003Cguid>`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>The only stable episode identifier; `isPermaLink` is often a lie\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003CpubDate>`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>RFC 822 format, frequently wrong or timezone-naive\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003Cenclosure url length type>`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>Audio URL, byte size, MIME type. Do not download it\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003Citunes:duration>`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>Seconds, or `HH:MM:SS`, or `MM:SS`. Handle all three\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`\u003Citunes:episode>`, `\u003Citunes:season>`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>Real ordering when `pubDate` is unreliable\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cpre>\u003Ccode class=\"language-python\">import feedparser\n\nfeed = feedparser.parse(xml_body) # xml_body = raw XML from your fetcher\n\nshow = {\n "title": feed.feed.get("title"),\n "author": feed.feed.get("author"),\n "type": feed.feed.get("itunes_type", "episodic"),\n "categories": [t["term"] for t in feed.feed.get("tags", [])],\n "episode_count": len(feed.entries),\n}\n\nepisodes = [{\n "guid": e.get("id"),\n "title": e.get("title"),\n "published": e.get("published_parsed"),\n "duration": e.get("itunes_duration"),\n "audio_bytes": int(e.enclosures[0].get("length", 0)) if e.enclosures else None,\n} for e in feed.entries]\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Two field-level gotchas that cost people a week each. First, \u003Ccode>\u003Citunes:duration>\u003C\u002Fcode> has three legal formats in the wild and a nontrivial number of feeds report zero. Second, the \u003Ccode>length\u003C\u002Fcode> attribute on \u003Ccode>\u003Cenclosure>\u003C\u002Fcode> is supposed to be the file size in bytes, but plenty of hosts emit \u003Ccode>0\u003C\u002Fcode> or a stale value, so never treat it as a reliable audio-length proxy. If you need duration you need the tag, and if the tag is missing you accept a null rather than infer one.\u003C\u002Fp>\n\u003Cp>Feeds are also frequently large. A long-running daily show can carry 3,000 items in a single XML document running past 15 MB. Stream-parse with \u003Ccode>lxml.etree.iterparse\u003C\u002Fcode> rather than loading the whole tree if you are sweeping a wide catalogue.\u003C\u002Fp>",3,[],{"id":154,"title":155,"anchor":156,"content_html":157,"image":15,"image_alt":15,"item_order":158,"parent_link_num":26,"children":159},4444,"The Podcasting 2.0 Namespace","the-podcasting-20-namespace","\u003Cp>This is the part most media analytics stacks skip, and it is free structured data sitting in feeds they already fetch.\u003C\u002Fp>\n\u003Cp>The \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fgithub.com\u002FPodcastindex-org\u002Fpodcast-namespace\u002Fblob\u002Fmain\u002Fdocs\u002F1.0.md\">Podcast Namespace\u003C\u002Fa> is a community-maintained extension, declared as \u003Ccode>xmlns:podcast=\"https:\u002F\u002Fpodcastindex.org\u002Fnamespace\u002F1.0\"\u003C\u002Fcode>. Adoption is partial but rising, and the tags that actually appear in production feeds are worth handling:\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Tag\u003C\u002Fth>\u003Cth>Level\u003C\u002Fth>\u003Cth>What you get\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>`podcast:guid`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>A stable show identifier that survives a feed URL change\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:transcript`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>URL plus MIME type for a VTT, SRT or JSON transcript\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:chapters`\u003C\u002Ftd>\u003Ctd>Item\u003C\u002Ftd>\u003Ctd>JSON chapter file with segment titles and timestamps\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:person`\u003C\u002Ftd>\u003Ctd>Both\u003C\u002Ftd>\u003Ctd>Named hosts and guests with roles, the closest thing to a guest graph\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:funding`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>Where the show monetises, a direct business-model signal\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:location`\u003C\u002Ftd>\u003Ctd>Both\u003C\u002Ftd>\u003Ctd>Geographic subject of the show or episode\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:locked`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>Whether the owner has blocked automated feed migration\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:medium`\u003C\u002Ftd>\u003Ctd>Channel\u003C\u002Ftd>\u003Ctd>`podcast`, `music`, `audiobook`, `newsletter`, distinguishes format\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>\u003Ccode>podcast:guid\u003C\u002Fcode> deserves special attention. The spec defines it as a UUID version 5 generated over the feed URL with the protocol scheme and any trailing slashes stripped, using the fixed namespace UUID \u003Ccode>ead4c236-bf58-58c6-a2c6-a6b28d128cb6\u003C\u002Fcode>. That means you can compute the expected GUID for any feed yourself and use it as a join key, even for feeds that do not publish the tag:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">import uuid\n\nPODCAST_NS = uuid.UUID("ead4c236-bf58-58c6-a2c6-a6b28d128cb6")\n\ndef podcast_guid(feed_url: str) -> str:\n stripped = feed_url.split(":\u002F\u002F", 1)[-1].rstrip("\u002F")\n return str(uuid.uuid5(PODCAST_NS, stripped))\n\nprint(podcast_guid("https:\u002F\u002Ffeeds.sparkproxy.io\u002Fshow.xml"))\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>For a media analytics product, that one function solves the deduplication problem that otherwise haunts every podcast dataset: the same show reachable through four different tracking-prefixed URLs, counted four times.\u003C\u002Fp>\n\u003Cp>\u003Ccode>podcast:person\u003C\u002Fcode> is the sleeper. If you track guest appearances across a media landscape, the way you would track byline networks in \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fproxies-for-news-and-media-monitoring\u002F\">news and media monitoring\u003C\u002Fa>, the tag hands you a name, a role and often a URL without any named entity recognition at all.\u003C\u002Fp>",4,[],{"id":161,"title":162,"anchor":163,"content_html":164,"image":15,"image_alt":15,"item_order":129,"parent_link_num":26,"children":165},4445,"Why Chart Position Must Be Collected Per Country","why-chart-position-must-be-collected-per-country","\u003Cp>Here is the fact that reshapes the pipeline: there is no such thing as \"the podcast chart\". There are roughly 175 Apple storefronts, each with its own Top Shows list and its own per-category lists, and they diverge wildly.\u003C\u002Fp>\n\u003Cp>A show with a concentrated audience in a small market routinely charts top ten there and nowhere else. A US-heavy show may be invisible in every other storefront. Ranking is also relative to a storefront's total listening volume, so #20 in the United States and #1 in Iceland are not comparable achievements, and any dashboard that averages ranks across countries into one number is producing a meaningless figure.\u003C\u002Fp>\n\u003Cp>Apple does not publish the ranking formula. What it has said publicly is that the Top Shows chart reflects recent follower and listener activity rather than lifetime download totals, which is why a show can drop 40 places in a week without losing a single subscriber. Treat rank as a momentum signal, not a size signal.\u003C\u002Fp>\n\u003Ch4 id=\"apple-s-public-per-storefront-json\">Apple's public per-storefront JSON\u003C\u002Fh4>\n\u003Cp>Most teams scrape \u003Ccode>podcasts.apple.com\u003C\u002Fcode> HTML for this. You usually do not have to. This is the classic case for \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fhow-to-scrape-hidden-json-api-endpoints\u002F\">finding the hidden JSON endpoint\u003C\u002Fa> behind a rendered page: Apple's Marketing Tools RSS service publishes chart data as clean JSON, keyed by storefront in the path.\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-bash\"># Top 100 shows in the US storefront\ncurl -s "https:\u002F\u002Frss.marketingtools.apple.com\u002Fapi\u002Fv2\u002Fus\u002Fpodcasts\u002Ftop\u002F100\u002Fpodcasts.json"\n\n# The same list for Germany\ncurl -s "https:\u002F\u002Frss.marketingtools.apple.com\u002Fapi\u002Fv2\u002Fde\u002Fpodcasts\u002Ftop\u002F100\u002Fpodcasts.json"\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Each entry carries the collection ID, name, artist and artwork URL. Pair that ID with the Lookup call from earlier and you have chart rank joined to a feed URL. Sweeping every storefront daily is a few hundred requests, which is exactly the volume where a single origin IP starts collecting 403s and where distributed egress stops being optional.\u003C\u002Fp>\n\u003Ch4 id=\"spotify-and-the-rest\">Spotify and the rest\u003C\u002Fh4>\n\u003Cp>Spotify's Web API has \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fdeveloper.spotify.com\u002Fdocumentation\u002Fweb-api\u002Freference\u002Fget-a-show\">Get Show\u003C\u002Fa> and episode endpoints, requires OAuth client credentials, and takes a \u003Ccode>market\u003C\u002Fcode> parameter that changes availability but does not return chart position. Spotify's charts live on a separate public web property, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fpodcastcharts.byspotify.com\u002F\">podcastcharts.byspotify.com\u003C\u002Fa>, broken out by country and refreshed weekly rather than daily. Different cadence, different methodology, not comparable to Apple's numbers.\u003C\u002Fp>\n\u003Cp>So your chart table needs a composite key of \u003Ccode>(platform, storefront, chart_type, category, captured_at)\u003C\u002Fcode>. Anyone who models it as \u003Ccode>show_id -> rank\u003C\u002Fcode> has already lost the data.\u003C\u002Fp>\n\u003Ch4 id=\"where-the-ip-actually-matters\">Where the IP actually matters\u003C\u002Fh4>\n\u003Cp>Be precise about this, because vendors oversell it. The Apple Marketing Tools endpoint takes the storefront in the URL, so a German IP is not required to read the German chart. What geo-distributed IPs genuinely buy you:\u003C\u002Fp>\n\u003Col>\n\u003Cli>\u003Cstrong>Rate limit headroom.\u003C\u002Fstrong> 175 storefronts multiplied by dozens of categories, run daily, is tens of thousands of requests from one address.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Availability checks.\u003C\u002Fstrong> Whether a show is listed at all in a storefront, and what artwork or description a local listener sees, is served by IP-inferred surfaces on the web properties.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Localised web pages.\u003C\u002Fstrong> \u003Ccode>podcasts.apple.com\u003C\u002Fcode> and Spotify's web player render country-specific copy, subscription pricing, and different recommendation modules.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Region-restricted catalogue.\u003C\u002Fstrong> Rights-limited and explicit-content-filtered shows disappear entirely from certain storefronts, and that disappearance is only observable from inside.\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cp>Points 2 through 4 are the same problem covered in \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fwhat-does-geo-targeting-mean-in-proxies\u002F\">geo-targeting with proxies\u003C\u002Fa>, and they are why a podcast analytics stack looks structurally like an app store data pipeline. If you have built \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fscrape-app-store-and-google-play-data\u002F\">App Store and Google Play collection\u003C\u002Fa> before, the storefront fan-out pattern will feel familiar.\u003C\u002Fp>",[],{"id":167,"title":168,"anchor":169,"content_html":170,"image":15,"image_alt":15,"item_order":171,"parent_link_num":26,"children":172},4446,"Episode and Catalogue Change Detection","episode-and-catalogue-change-detection","\u003Cp>Refetching millions of feeds on a fixed schedule is wasteful and gets you throttled. Change detection is the difference between a pipeline that costs a fortune and one that does not.\u003C\u002Fp>\n\u003Ch4 id=\"conditional-requests-first\">Conditional requests first\u003C\u002Fh4>\n\u003Cp>Podcast hosts are unusually good about HTTP caching headers, because they serve feeds to millions of podcast apps. Use them. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fwww.rfc-editor.org\u002Frfc\u002Frfc9110#name-conditional-requests\">RFC 9110 conditional requests\u003C\u002Fa> define the contract:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">import requests\n\ndef poll(feed_url, etag=None, last_modified=None):\n headers = {"User-Agent": "SparkProxyPodcastBot\u002F1.0 (+https:\u002F\u002Fwww.sparkproxy.io\u002Fbot)"}\n if etag:\n headers["If-None-Match"] = etag\n if last_modified:\n headers["If-Modified-Since"] = last_modified\n\n r = requests.get(feed_url, headers=headers, timeout=45)\n if r.status_code == 304:\n return None, etag, last_modified # unchanged, a few hundred bytes spent\n return r.text, r.headers.get("ETag"), r.headers.get("Last-Modified")\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>In a large sweep the majority of daily polls come back 304. That single header pair cuts bandwidth dramatically and drops your visible request weight enough that hosts stop noticing you.\u003C\u002Fp>\n\u003Ch4 id=\"push-instead-of-poll\">Push instead of poll\u003C\u002Fh4>\n\u003Cp>Two mechanisms let you skip polling for participating hosts. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fwebsub\u002F\">WebSub\u003C\u002Fa> is advertised in the feed as \u003Ccode>\u003Catom:link rel=\"hub\" href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002F...\">\u003C\u002Fcode> and gives you a callback on publish. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fpodping.org\u002F\">Podping\u003C\u002Fa> is podcast-specific: hosts write a lightweight notification when a feed updates, and consumers watch that stream instead of crawling. Neither covers the whole ecosystem, so they reduce your polling load rather than replacing it.\u003C\u002Fp>\n\u003Ch4 id=\"diffing-the-catalogue\">Diffing the catalogue\u003C\u002Fh4>\n\u003Cp>When a feed does change, compare GUID sets rather than diffing raw XML:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">def diff_catalogue(previous_guids: set, current: list) -> dict:\n current_guids = {e["guid"] for e in current}\n return {\n "new": sorted(current_guids - previous_guids),\n "removed": sorted(previous_guids - current_guids),\n "retained": len(current_guids & previous_guids),\n }\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>The \u003Ccode>removed\u003C\u002Fcode> bucket is the interesting one, and it is the metric nobody tracks. Episodes vanish from feeds constantly: sponsorship expiry, legal takedowns, quiet retractions after a bad guest, or a host truncating the feed to the most recent 300 items. A silent unpublish is often a genuine news event, and you only catch it if you retain your own history of the catalogue.\u003C\u002Fp>\n\u003Cp>Three more mutations to watch:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Title and description edits\u003C\u002Fstrong> on existing GUIDs, usually SEO tuning or a correction.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Enclosure URL changes\u003C\u002Fstrong> on an unchanged GUID, which normally means a re-upload with a different ad load rather than new content.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>\u003Ccode>\u003Citunes:new-feed-url>\u003C\u002Fcode> appearing at channel level\u003C\u002Fstrong>, the standard signal that the show has migrated hosts. Follow it once and update your canonical URL, or your record goes stale within a month.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Rate limiting discipline applies throughout. The \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fguide-on-ethical-scraping-and-rate-limiting\u002F\">ethical scraping and rate limiting guide\u003C\u002Fa> covers the general shape. For feeds specifically, cap concurrency per host domain rather than globally, since a handful of large hosting providers serve a very long tail of shows.\u003C\u002Fp>",6,[],{"id":174,"title":175,"anchor":176,"content_html":177,"image":15,"image_alt":15,"item_order":178,"parent_link_num":26,"children":179},4447,"Collecting Podcast Data With the SparkProxy Scraping API","collecting-podcast-data-with-the-sparkproxy-scraping-api","\u003Cp>Raw XML feeds usually need nothing more than a rotating pool and a sane User-Agent. The chart and storefront surfaces are where a managed layer pays for itself, because they are JavaScript-rendered and country-sensitive. Parameters below come from the \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fdocs\u002Fscraping-api\u002F\">SparkProxy Scraping API docs\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>A plain feed fetch through a rotating exit, no rendering needed:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-bash\">curl -X GET "https:\u002F\u002Fscrape.sparkproxy.io\u002Fapi\u002Fv1?url=https:\u002F\u002Ffeeds.sparkproxy.io\u002Fshow.xml&render_js=false" \\\n -H "X-API-Key: sk-xxxxxxxxxxxxxxxx"\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Feed polling in Python, keeping the conditional-request logic intact:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">import requests\n\nAPI = "https:\u002F\u002Fscrape.sparkproxy.io\u002Fapi\u002Fv1"\nAPI_KEY = "sk-xxxxxxxxxxxxxxxx"\n\ndef fetch_feed(feed_url, country=None):\n params = {"url": feed_url, "render_js": "false", "format": "json"}\n if country:\n params["country_code"] = country # ISO alpha-2: US, GB, DE\n r = requests.get(API, params=params, headers={"X-API-Key": API_KEY}, timeout=90)\n data = r.json()\n return data["status_code"], data["body"], data["credits_used"]\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Pulling the Apple chart JSON for one storefront:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">def apple_chart(storefront: str, limit: int = 100):\n url = (f"https:\u002F\u002Frss.marketingtools.apple.com\u002Fapi\u002Fv2\u002F{storefront}"\n f"\u002Fpodcasts\u002Ftop\u002F{limit}\u002Fpodcasts.json")\n r = requests.get(API, params={"url": url, "format": "json"},\n headers={"X-API-Key": API_KEY}, timeout=60)\n return r.json()["body"]\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Checking how a show's storefront page renders to a listener in Japan, which needs a real browser and a local exit:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-bash\">curl -X GET "https:\u002F\u002Fscrape.sparkproxy.io\u002Fapi\u002Fv1?url=https:\u002F\u002Fpodcasts.apple.com\u002Fjp\u002Fpodcast\u002Fid1200361736&country_code=JP&render_js=true&wait_for=3000&format=html" \\\n -H "X-API-Key: sk-xxxxxxxxxxxxxxxx"\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>For storefronts that block datacenter ranges, route through the residential tier and pin a session so a multi-step check stays on one IP:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-python\">params = {\n "url": "https:\u002F\u002Fpodcastcharts.byspotify.com\u002F",\n "country_code": "BR",\n "render_js": "true",\n "premium_proxy": "true",\n "session_id": "spotify-charts-br",\n "wait_for": "4000",\n "format": "html",\n}\nr = requests.get(API, params=params, headers={"X-API-Key": API_KEY}, timeout=120)\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Capturing dated visual evidence of a chart position, useful when a client disputes what ranked where:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-bash\">curl -X GET "https:\u002F\u002Fscrape.sparkproxy.io\u002Fapi\u002Fv1?url=https:\u002F\u002Fpodcasts.apple.com\u002Fie\u002Fcharts&country_code=IE&render_js=true&format=screenshot" \\\n -H "X-API-Key: sk-xxxxxxxxxxxxxxxx" \\\n --output chart_ie_2026-08-18.png\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Cost note: \u003Ccode>render_js\u003C\u002Fcode> and \u003Ccode>premium_proxy\u003C\u002Fcode> both cost more per request. Feed polls are the bulk of your volume and need neither, so keep them on the cheap path and reserve the expensive path for chart and storefront sweeps. The response returns \u003Ccode>credits_used\u003C\u002Fcode> per call, which makes it easy to spot when someone has accidentally left rendering enabled on the feed crawler.\u003C\u002Fp>",7,[],{"id":181,"title":182,"anchor":183,"content_html":184,"image":15,"image_alt":15,"item_order":185,"parent_link_num":26,"children":186},4448,"The Honest Limit: You Cannot Scrape Downloads","the-honest-limit-you-cannot-scrape-downloads","\u003Cp>This is the section that should decide whether you trust a podcast analytics vendor.\u003C\u002Fp>\n\u003Cp>A podcast download is counted server-side, at the moment a client requests bytes from the audio file. That request goes to the hosting provider's origin or CDN, and the count lives in their logs. There is no public endpoint, no rendered page, and no undocumented JSON that exposes it. The \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fiabtechlab.com\u002Fstandards\u002Fpodcast-measurement-guidelines\u002F\">IAB Tech Lab Podcast Measurement Technical Guidelines\u003C\u002Fa> define what counts as a download at all, including the byte threshold served and the filtering of known bots, and compliance is certified per hosting platform. Nothing in that process touches a surface a crawler can reach.\u003C\u002Fp>\n\u003Cp>The same applies to platform-side listening. Apple Podcasts Connect and Spotify for Creators report plays, followers, completion and drop-off only to the verified owner of the show, behind authentication. Scraping those dashboards would require credentials you do not have, and using someone else's would be unauthorised access, not data collection.\u003C\u002Fp>\n\u003Cp>So what can you legitimately say from public data?\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Public signal\u003C\u002Fth>\u003Cth>What it genuinely indicates\u003C\u002Fth>\u003Cth>What it does not indicate\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Chart rank in a storefront\u003C\u002Ftd>\u003Ctd>Recent momentum in follows and listens, relative to that market\u003C\u002Ftd>\u003Ctd>Audience size, or comparability across countries\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Chart persistence over weeks\u003C\u002Ftd>\u003Ctd>Sustained engagement rather than a launch spike\u003C\u002Ftd>\u003Ctd>Any absolute number\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Publishing cadence and gaps\u003C\u002Ftd>\u003Ctd>Production health, resourcing, likely hiatus\u003C\u002Ftd>\u003Ctd>Listenership trend\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Review and rating counts by storefront\u003C\u002Ftd>\u003Ctd>Rough engaged-audience floor and geographic spread\u003C\u002Ftd>\u003Ctd>Downloads. Review rates vary hugely by genre\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Ad load, derived from episode duration drift\u003C\u002Ftd>\u003Ctd>Monetisation intensity\u003C\u002Ftd>\u003Ctd>Revenue\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>`podcast:funding` and sponsor mentions\u003C\u002Ftd>\u003Ctd>Business model and active advertisers\u003C\u002Ftd>\u003Ctd>Deal value\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Every serious estimate of podcast audience size is a model fitted on these signals against a small set of shows whose real numbers are known through a partnership or a public disclosure. That is legitimate work, and it is also inference. Label it as an estimate and give it a confidence interval. If a dataset presents a download figure for a show it has no relationship with, and no interval on the number, the number was modelled, whatever the marketing says.\u003C\u002Fp>\n\u003Cp>Saying this plainly is not a weakness in your product. It is the thing that makes the rest of your numbers believable.\u003C\u002Fp>",8,[],{"id":188,"title":189,"anchor":190,"content_html":191,"image":15,"image_alt":15,"item_order":192,"parent_link_num":26,"children":193},4449,"Audio, Transcripts and Rights","audio-transcripts-and-rights","\u003Cp>The boundary here is clean, and staying inside it costs you nothing analytically.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Do not download the audio.\u003C\u002Fstrong> The \u003Ccode>\u003Cenclosure>\u003C\u002Fcode> URL is public, but the audio file is a copyrighted work, and pulling it at scale also inflates the publisher's download counts and their bandwidth bill. You would be corrupting the exact metric everyone else depends on. Read the metadata, skip the media.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Transcripts carry their own rights questions.\u003C\u002Fstrong> A \u003Ccode>podcast:transcript\u003C\u002Fcode> tag links a file the publisher chose to make available, typically WebVTT under the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\u002F\u002Fwww.w3.org\u002FTR\u002Fwebvtt1\u002F\">W3C WebVTT specification\u003C\u002Fa> or SRT. Published deliberately for accessibility is not the same as licensed for redistribution. The transcript is a derivative of a copyrighted work, so republishing it wholesale, or training on a corpus of them, is a licensing decision rather than a technical one. Reading a transcript to extract topics, entities or sponsor mentions for analysis is a very different act from serving that transcript as your own content.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Generating your own transcripts is worse, not better.\u003C\u002Fstrong> Running speech-to-text over the audio means you first made a copy of the audio. The output is still derived from the original work.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Respect the mechanical signals.\u003C\u002Fstrong> Check \u003Ccode>robots.txt\u003C\u002Fcode> on the host serving the feed, honour \u003Ccode>podcast:locked\u003C\u002Fcode> when the owner has blocked automated migration, and send a User-Agent with a contact URL so anyone who wants to talk to you can. The same courtesy that applies to \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fusing-proxies-for-social-media-monitoring\u002F\">social media monitoring\u003C\u002Fa> applies here, with the added point that podcast publishers are individually much smaller and much more likely to notice you.\u003C\u002Fp>\n\u003Cp>None of this stops you building a strong product. Feed metadata, Podcasting 2.0 tags, per-storefront chart history and catalogue change detection give you a picture of the medium that no single platform dashboard offers. The constraint is only on audio, and on claiming private numbers you do not have.\u003C\u002Fp>",9,[],{"id":195,"title":196,"anchor":197,"content_html":198,"image":15,"image_alt":15,"item_order":199,"parent_link_num":26,"children":200},4450,"About the Author","about-the-author","\u003Cp>The \u003Cstrong>SparkProxy Technical Team\u003C\u002Fstrong> builds and operates the infrastructure behind SparkProxy's datacenter proxies, residential proxies, and the SparkProxy Scraping API. We work daily with customers running geo-distributed collection across storefronts, feeds and public APIs, and we write from what those pipelines actually do in production. Full API parameters and pricing are in the \u003Ca href=\"https:\u002F\u002Fwww.sparkproxy.io\u002Fdocs\u002Fscraping-api\u002F\">Scraping API documentation\u003C\u002Fa>. Questions about a specific collection architecture: support@sparkproxy.io.\u003C\u002Fp>",10,[],{"title":12,"description":13,"keywords":50,"canonical":11,"og_image":15,"article_published":27,"article_modified":28},{"ok":5,"data":203},{"featured":204,"recommended":261,"popular":348,"trending":421,"tags":513},[205,221,241],{"id":206,"slug":207,"url":208,"title":209,"summary":210,"image":211,"category":217,"author":218,"post_type":25,"views":199,"published_at":219,"updated_at":220},51,"datacenter-proxies-for-cybersecurity-threat-intelligence","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fdatacenter-proxies-for-cybersecurity-threat-intelligence","Threat Intelligence Proxies: OSINT & Dark Web Recon","Learn how datacenter proxies power threat intelligence: OSINT collection, dark web monitoring, and malware analysis without exposing your infrastructure.",{"big":212,"default":213,"slider":214,"mid":215,"small":216,"alt":209},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202606\u002Fimage_870x580_6a33955e731a7.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202606\u002Fimage_870x_6a33955f61a5f.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202606\u002Fimage_694x532_6a33956023aaa.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202606\u002Fimage_430x256_6a339560aea05.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202606\u002Fimage_140x98_6a3395611f6e8.webp",{"name":17,"slug":18,"color":19,"url":20},{"name":22,"slug":23,"url":24},"2026-06-18T12:19:05+05:30","2026-06-18T12:22:22+05:30",{"id":222,"slug":223,"url":224,"title":225,"summary":226,"image":227,"category":233,"author":237,"post_type":25,"views":238,"published_at":239,"updated_at":240},19,"what-is-proxy-server","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fwhat-is-proxy-server","What Is a Proxy Server? Meaning & How It Works (2026)","A proxy server is an intermediary between your device and the internet, it hides your IP, filters traffic, and boosts security for 65%+ of enterprises.",{"big":228,"default":229,"slider":230,"mid":231,"small":232,"alt":225},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_870x580_6a06ac27a665d.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_870x_6a06ac28c2130.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_694x532_6a06ac29b3fcc.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_430x256_6a06ac2a8a74f.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_140x98_6a06ac2b0ceb5.webp",{"name":234,"slug":235,"color":19,"url":236},"Proxy Basic","proxy-basic","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fproxy-basic",{"name":22,"slug":23,"url":24},232,"2026-05-15T10:46:54+05:30","2026-05-15T10:46:41+05:30",{"id":242,"slug":243,"url":244,"title":245,"summary":246,"image":247,"category":253,"author":257,"post_type":25,"views":258,"published_at":259,"updated_at":260},14,"how-to-build-a-captcha-solver-with-machine-learning","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fhow-to-build-a-captcha-solver-with-machine-learning","CAPTCHA Solver with Machine Learning: Build & Troubleshoot (2026)","Build a CAPTCHA solver with machine learning in Python. CNN models hit 95%+ accuracy on text CAPTCHAs. Step-by-step code, model comparison & troubleshooting.",{"big":248,"default":249,"slider":250,"mid":251,"small":252,"alt":245},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_870x580_69ff7aa947352.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_870x_69ff7aaa67dc6.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_694x532_69ff7aac5ab3b.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_430x256_69ff7aad65722.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202605\u002Fimage_140x98_69ff7aae08194.webp",{"name":254,"slug":255,"color":19,"url":256},"Guides","guides","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fguides",{"name":22,"slug":23,"url":24},233,"2026-05-09T23:53:54+05:30","2026-05-09T23:52:46+05:30",[262,279,296,313,333],{"id":263,"slug":264,"url":265,"title":266,"summary":267,"image":268,"category":274,"author":275,"post_type":25,"views":276,"published_at":277,"updated_at":278},140,"how-to-bypass-kasada","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fhow-to-bypass-kasada","How to Bypass Kasada When Web Scraping","Bypass Kasada when scraping public data: how KPSDK proof-of-work, the x-kpsdk-ct and x-kpsdk-cd headers, and the 429 challenge work, plus real API code.",{"big":269,"default":270,"slider":271,"mid":272,"small":273,"alt":266},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_870x580_6a925ea519b4f.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_870x_6a925ea5b95b9.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_694x532_6a925ea63450a.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_430x256_6a925ea6b6494.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_140x98_6a925ea719974.webp",{"name":254,"slug":255,"color":19,"url":256},{"name":22,"slug":23,"url":24},49,"2026-07-30T14:00:01+05:30","2026-08-29T09:53:01+05:30",{"id":280,"slug":281,"url":282,"title":283,"summary":284,"image":285,"category":291,"author":292,"post_type":25,"views":293,"published_at":294,"updated_at":295},126,"how-to-bypass-perimeterx","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fhow-to-bypass-perimeterx","How to Bypass PerimeterX (HUMAN Security) When Scraping","Bypass PerimeterX (HUMAN) when scraping public data: how _px cookies, the sensor JS, and Press and Hold work, plus real browsers, residential IPs, and API code.",{"big":286,"default":287,"slider":288,"mid":289,"small":290,"alt":283},"https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_870x580_6a925eaa150dc.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_870x_6a925eaa9e6c8.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_694x532_6a925eab087e7.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_430x256_6a925eab6b6f6.webp","https:\u002F\u002Fblog.cms.sparkproxy.io\u002Fuploads\u002Fimages\u002F202608\u002Fimage_140x98_6a925eabbad1b.webp",{"name":254,"slug":255,"color":19,"url":256},{"name":22,"slug":23,"url":24},44,"2026-07-28T14:00:02+05:30","2026-08-29T09:53:06+05:30",{"id":297,"slug":298,"url":299,"title":300,"summary":301,"image":302,"category":308,"author":309,"post_type":25,"views":310,"published_at":311,"updated_at":312},31,"guide-to-proxy-dns-leak-testing-and-mitigation","https:\u002F\u002Fwww.sparkproxy.io\u002Fblog\u002Fguide-to-proxy-dns-leak-testing-and-mitigation","Guide to Proxy DNS Leak Testing and Mitigation","DNS leaks expose your queries to ISPs even when using a proxy. 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