DEV Community: Can Yılmaz

Building a Letterboxd Film & Review data pipeline: from raw scrape to first insight

Can Yılmaz — Fri, 15 May 2026 13:38:02 +0000

When you need Letterboxd Film & Review as a recurring feed, the gap between "got a few rows out" and "have a clean nightly dataset in the warehouse" is wider than it looks. Here is the pipeline I sketched out, with the decisions I made at each step.

Source survey

Letterboxd Scraper Films, Ratings, Reviews & User Data Scrape films, ratings, cast & crew, genres, and user reviews from Letterboxd, the world's leading social film-discovery platform. For pipeline purposes, the relevant questions are: how stable is the source markup, what is the natural pagination unit, and how aggressively does it rate-limit. For this source the answer is "stable enough, list-based pagination, moderate rate-limiting" -- which makes it a good candidate for a daily incremental job rather than a streaming one.

Output schema

The actor I used emits records with these fields:

type -- type
filmSlug -- film slug
title -- title
year -- year
director -- director
cast -- cast
genres -- genres
runtime -- runtime
averageRating -- average rating
ratingsCount -- ratings count
language -- language
country -- country
synopsis -- synopsis
posterUrl -- poster url
filmUrl -- film url
embeddedReviewCount -- embedded review count
scrapedAt -- scraped at
reviews -- reviews

For warehouse ingestion I would keep this almost as-is. Promote the obvious identifier field to a primary key, cast the timestamp columns to native types, and stash any deeply nested or free-text fields in a TEXT column rather than trying to normalise them.

Sample records

A peek at two raw rows from a sample run:

{
  "type": "film",
  "filmSlug": "the-godfather",
  "title": "The Godfather",
  "year": "1972",
  "director": [
    "Francis Ford Coppola"
  ],
  "cast": [
    "Marlon Brando",
    "Al Pacino",
    "... (8 more)"
  ],
  "genres": [
    "Crime",
    "Drama"
  ],
  "runtime": "175 mins",
  "averageRating": 4.52,
  "ratingsCount": 2666451
}

The flat structure is forgiving. You can drop this straight into a staging table with CREATE TABLE ... AS SELECT * FROM read_json_auto(...) in DuckDB, or pd.json_normalize(rows) in Python, and the downstream model layer barely needs any work.

Pipeline stages

For community managers, trend researchers and brand-monitoring teams this is the rough shape I would build:

Extract: schedule the scraper to run every N hours, write the raw JSON to object storage partitioned by date.
Land: load the raw JSON into a staging table with minimal type coercion -- you want to be able to replay history without re-scraping.
Transform: dedupe on the natural key, enrich with reference data, surface a curated view for social listening, sentiment tracking, brand monitoring and content research.
Serve: expose a thin API or dashboard on the curated view. This is the layer your stakeholders actually touch.

Operational considerations

Three things bite people on these pipelines: schema drift in the upstream source, duplicate records from overlapping scrape windows, and quietly failing runs. Wire up record-count assertions early -- a sudden 50% drop is almost always a sign that the site changed and your selectors need a refresh, not a real shift in supply.

Tooling choices

A few opinionated picks I would default to for this kind of pipeline: object storage (S3, GCS, R2) for the raw landing zone because it is cheap and replayable; a columnar warehouse (BigQuery, Snowflake, DuckDB if you are small) for the staging and curated layers because the analytical queries you will run over this dataset are pretty much exclusively column-scans; a tiny dbt or SQLMesh project for the transformations because version-controlled, tested SQL is much nicer to maintain than ad-hoc queries; and a workflow orchestrator (Airflow, Prefect, GitHub Actions on a cron) for scheduling. None of those are exotic choices, which is the point -- the boring stack is the right stack for a feed like this.

Verdict

For a single-source feed like Letterboxd Film & Review, the work is mostly in the staging and dedup logic. The extraction itself is a solved problem if you do not insist on rolling your own crawler. Once the data is landing reliably, the analytical layer is where you spend your time -- and that is the layer where the dataset actually pays for itself.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/letterboxd-film-review-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Sample dataset analysis: a 30-row snapshot of KuCoin Market

Can Yılmaz — Fri, 15 May 2026 13:32:48 +0000

I pulled a 30-row sample of KuCoin Market to see whether the dataset is rich enough to support back-testing strategies, monitoring liquidity, building risk dashboards and feeding price-discovery models, or whether it is the kind of feed you have to enrich heavily before it becomes useful. Short answer: richer than I expected. Long answer below.

What is in the sample

KuCoin Market Scraper Live Crypto Prices for All Pairs to JSON & CSV Scrape live cryptocurrency market data from KuCoin, one of the world's leading crypto exchanges, straight from its official public API. Each record has the following fields:

symbol -- symbol
baseCurrency -- base currency
quoteCurrency -- quote currency
lastPrice -- last price
openPrice -- open price
high24h -- high24h
low24h -- low24h
priceChangePercent24h -- price change percent24h
priceChange24h -- price change24h
volume24h -- volume24h
volumeValue24h -- volume value24h
bidPrice -- bid price
askPrice -- ask price
averagePrice -- average price
scrapedAt -- scraped at

The fields divide into three groups: identifiers (stable across re-scrapes), descriptive content (the actual signal you want), and metadata (timestamps, source URLs, scrape provenance). For most analytical workflows you only really touch the middle group, but the identifiers matter the moment you start joining across runs.

Two example records

Here are two rows from the sample, trimmed slightly so they fit:

{
  "symbol": "BTC-USDT",
  "baseCurrency": "BTC",
  "quoteCurrency": "USDT",
  "lastPrice": 81316,
  "openPrice": 79048.2,
  "high24h": 81316.1,
  "low24h": 78771.9,
  "priceChangePercent24h": 2.86,
  "priceChange24h": 2267.8,
  "volume24h": 2639.565563620241
}

{
  "symbol": "ETH-USDT",
  "baseCurrency": "ETH",
  "quoteCurrency": "USDT",
  "lastPrice": 2297.37,
  "openPrice": 2244.22,
  "high24h": 2299.5,
  "low24h": 2234.11,
  "priceChangePercent24h": 2.36,
  "priceChange24h": 53.15,
  "volume24h": 80164.34178326
}

Even without aggregation you can see the cardinality is interesting. The descriptive fields vary widely across rows, which means a 30-row sample is enough to do meaningful exploratory analysis but probably not enough for any production-grade modelling -- you would want at least an order of magnitude more.

What I would do with the data

A non-exhaustive list of analyses this dataset directly supports:

Frequency analysis on the categorical columns to spot dominant clusters and long-tail outliers.
Time-series breakdowns using the timestamp fields to see daily, weekly and seasonal patterns.
Text analysis on the free-form fields -- topic modelling, keyword extraction, sentiment if the content warrants it.
Cross-joins with external reference data (back-testing strategies, monitoring liquidity, building risk dashboards and feeding price-discovery models typically needs a second-source enrichment step) to produce something more valuable than either input alone.

Quirks I noticed

A few practical observations from poking at the rows:

Some optional fields are missing rather than null. Normalise on load.
Long-form text occasionally contains newlines and the odd unicode quirk; clean before tokenising.
Identifier-like fields are strings; do not let your warehouse coerce them to int.

How I would shape it for downstream use

If I were dropping this dataset into a warehouse the rough plan would be: stage the raw JSON unchanged in a landing zone partitioned by scrape date, then create a curated view that casts the identifier fields to strings, parses the timestamps as native DATE/TIMESTAMP types, splits any compound columns, and trims long-form text. Keeping that two-layer structure means you can replay history without re-scraping, and you can iterate on the curated schema without losing fidelity.

For analytical queries the curated view is what you point dashboards and notebooks at. Common patterns I would pre-build as additional models: a daily-rollup view aggregating numeric columns by the most useful categorical breakdown, a recency view filtered to the last N days for "what is new" dashboards, and a delta view that diffs the latest snapshot against yesterday so you can surface additions and removals cheaply.

Bottom line

For a sample pull it is more than enough to validate the use-case fit. If the analytical questions you want to answer are reasonable on a 30-row sample, the full dataset will comfortably answer them. The next step is a longer-horizon pull -- a week or two of recurring snapshots -- which lets you stop treating each row as a one-off and start treating the dataset as a feed with its own dynamics.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/kucoin-market-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Building a Komoot Hiking & Outdoor Routes data pipeline: from raw scrape to first insight

Can Yılmaz — Fri, 15 May 2026 13:27:24 +0000

When you need Komoot Hiking & Outdoor Routes as a recurring feed, the gap between "got a few rows out" and "have a clean nightly dataset in the warehouse" is wider than it looks. Here is the pipeline I sketched out, with the decisions I made at each step.

Source survey

Komoot Hiking & Outdoor Routes Scraper Scrape Komoot Routes by Location or Coordinates Scrape hiking routes, cycling tours and outdoor activities from Komoot, Europe's leading outdoor navigation platform with 200M+ planned routes across 50+ countries. For pipeline purposes, the relevant questions are: how stable is the source markup, what is the natural pagination unit, and how aggressively does it rate-limit. For this source the answer is "stable enough, list-based pagination, moderate rate-limiting" -- which makes it a good candidate for a daily incremental job rather than a streaming one.

Output schema

The actor I used emits records with these fields:

tourId -- tour id
name -- name
sport -- sport
status -- status
distanceM -- distance m
distanceKm -- distance km
durationMin -- duration min
elevationUp -- elevation up
elevationDown -- elevation down
difficulty -- difficulty
visitors -- visitors
ratingScore -- rating score
ratingCount -- rating count
startLat -- start lat
startLng -- start lng
startAlt -- start alt
surfaces -- surfaces
wayTypes -- way types
coverImage -- cover image
mapImageUrl -- map image url
highlightsCount -- highlights count
highlights -- highlights
createdAt -- created at
updatedAt -- updated at
url -- url
scrapedAt -- scraped at

Sample records

A peek at two raw rows from a sample run:

{
  "tourId": "e28260717",
  "name": "Wasserläufer Waalweg Mooserstegle – Wandern im Ötztal",
  "sport": "hike",
  "status": "public",
  "distanceM": "6160",
  "distanceKm": "6.16",
  "durationMin": "117",
  "elevationUp": "241",
  "elevationDown": "241",
  "difficulty": "easy"
}

{
  "tourId": "e985847069",
  "name": "Small tour at Moos in Passeier - Stieber Waterfall",
  "sport": "hike",
  "status": "public",
  "distanceM": "3202",
  "distanceKm": "3.20",
  "durationMin": "59",
  "elevationUp": "111",
  "elevationDown": "110",
  "difficulty": "easy"
}

Pipeline stages

For data engineers and analysts this is the rough shape I would build:

Extract: schedule the scraper to run every N hours, write the raw JSON to object storage partitioned by date.
Land: load the raw JSON into a staging table with minimal type coercion -- you want to be able to replay history without re-scraping.
Transform: dedupe on the natural key, enrich with reference data, surface a curated view for powering dashboards, feeding ML pipelines and answering ad-hoc analytical questions.
Serve: expose a thin API or dashboard on the curated view. This is the layer your stakeholders actually touch.

Operational considerations

Tooling choices

Verdict

For a single-source feed like Komoot Hiking & Outdoor Routes, the work is mostly in the staging and dedup logic. The extraction itself is a solved problem if you do not insist on rolling your own crawler. Once the data is landing reliably, the analytical layer is where you spend your time -- and that is the layer where the dataset actually pays for itself.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/komoot-hiking-outdoor-routes-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

What I learned scraping JSON-LD Schema & Meta Tag Extractor: schema, gotchas and the tooling that worked

Can Yılmaz — Fri, 15 May 2026 13:21:56 +0000

I had a short window this week to evaluate JSON-LD Schema & Meta Tag Extractor as a data source. Here is the condensed write-up of what the data looks like, what surprised me, and the bits of infrastructure that paid off.

The source

JSON-LD Schema & Meta Tag Extractor Scrape Schema.org, OpenGraph & Meta Tags Extract structured data and SEO metadata from any webpage in seconds. The relevant questions for any new source are always: is the markup stable, is pagination sensible, and how aggressively does it rate-limit. For this one, all three answers are "good enough that you can build on it" -- which is honestly more than I can say for a lot of supposedly easy targets.

The schema

What you get back per record:

url -- url
pageTitle -- page title
metaDescription -- meta description
jsonLd -- json ld
openGraph -- open graph
twitter -- twitter
scrapeDate -- scrape date

Nothing exotic, which is exactly what you want from a feed. Flat records, predictable keys, types you can guess from the names.

Real rows

Two records from a sample run, trimmed for the inevitable wall of text:

{
  "url": "https://www.allrecipes.com/recipe/158968/spinach-and-feta-turkey-burgers/",
  "pageTitle": "Spinach and Feta Turkey Burgers Recipe",
  "metaDescription": "These spinach and feta turkey burgers are moist and easy to make in one bowl with simple ingredients, shaped into patties, and cooked on a...",
  "jsonLd": [
    "[... 1 items ...]"
  ],
  "openGraph": {
    "type": "article",
    "site_name": "Allrecipes",
    "url": "https://www.allrecipes.com/recipe/158968/spinach-and-feta-turkey-burgers/",
    "title": "Spinach and Feta Turkey Burgers",
    "description": "These spinach and feta turkey burgers are moist and easy to make in one bowl with simple ingredients, shaped into patties, and cooked on a...",
    "...": "(1 more fields)"
  },
  "twitter": {
    "card": "summary_large_image",
    "site": "@allrecipes",
    "title": "Spinach and Feta Turkey Burgers",
    "description": "These spinach and feta turkey burgers are moist and easy to make in one bowl with simple ingredients, shaped into patties, and cooked on a...",
    "image": "https://www.allrecipes.com/thmb/cpf6Rics5oHGq1TZ1df5fEaImwM=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/1360550-582be362ee994..."
  },
  "scrapeDate": "2026-05-15T10:51:38.226Z"
}

Gotchas

A few things I would not have known without actually pulling data:

Optional fields disappear instead of being null. Not the end of the world, but it means every loader needs to be tolerant of missing keys.
Long-form text fields contain control characters. Newlines, tabs, the occasional rogue carriage return. Strip them at load time unless you actively want them.
Timestamps are UTC ISO-8601 which is great, but it does mean any local-time dashboard needs an explicit conversion.
Some numeric fields are emitted as strings. Cast on load.
Re-scraping with overlapping windows creates duplicates. Dedup on the natural ID.

What I would build next

A few directions this dataset would support nicely:

A daily snapshot pipeline that lands raw JSON into object storage, then materialises a curated table for dashboards.
A change-detection layer that computes row-level diffs between consecutive scrapes -- great for surfacing new and removed records.
A text-extraction layer over the long-form content fields, feeding into search or topic modelling.
A small validation suite that runs after every scrape: row count above a floor, key fields present in 100% of rows, timestamp parses cleanly. Cheap to write, catches schema drift in minutes instead of weeks.

Cost considerations

Worth thinking about before you commit. The dominant cost on a recurring feed is not the per-record extraction price -- it is the maintenance time when the upstream source changes. A solid heuristic: budget half a day per source per quarter for maintenance work, and twice that for sources with active anti-bot defences. If that maintenance budget is too steep for the value the dataset provides, the project is not a fit.

The other cost worth modelling is storage. Raw JSON partitioned by date is cheap if you compress it -- a few cents per gigabyte per month on most clouds -- but it stops being cheap if you forget about retention. Set a lifecycle policy that ages anything older than your useful replay window into a colder tier, and revisit the policy every few months.

Bottom line

For an afternoon's evaluation work this was time well spent. The dataset is structurally clean, the scraper handled rate-limits without me having to think about it, and the records are rich enough to start asking real questions immediately. If the upstream source stays stable for a quarter -- which is the realistic horizon for most public sources -- the cost-benefit of integrating this feed is firmly positive.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/json-ld-schema-meta-tag-extractor. It supports JSON, CSV and Excel exports and runs on a schedule.

Why Internshala Internship & Jobs data is more interesting than you would think

Can Yılmaz — Fri, 15 May 2026 13:16:33 +0000

On the surface, Internshala Internship & Jobs sounds like the kind of dataset you would file under "boring infrastructure data" -- the sort of thing that lives in a corner of a warehouse and gets queried twice a quarter. After spending a bit of time actually looking at it, I have changed my mind. Here is why.

What is in it

Internshala Internship & Jobs Scraper Scrape Internshala.com Listings to JSON/CSV Scrape internship and fresher job listings from Internshala.com, India's #1 career platform trusted by 400K+ companies with 200K+ active listings. Each record carries a fairly rich set of fields:

listingId -- listing id
listingType -- listing type
url -- url
title -- title
company -- company
companyUrl -- company url
location -- location
isRemote -- is remote
stipend -- stipend
stipendMin -- stipend min
stipendMax -- stipend max
duration -- duration
startDate -- start date
applyBy -- apply by
openings -- openings
applicants -- applicants
skills -- skills
perks -- perks
description -- description
isPartTime -- is part time
hasJobOffer -- has job offer
postedAt -- posted at
category -- category
scrapedAt -- scraped at

The interesting bit is the combination. Individually, none of these fields is exotic. Together, they describe an entity precisely enough that you can do real analytics on it -- segmentation, trend analysis, even simple anomaly detection -- without needing a second data source.

Two records from a sample run

{
  "listingId": "3150094",
  "listingType": "internships",
  "url": "https://internshala.com/internship/detail/work-from-home-web-development-internship-at-zdminds1778824887",
  "title": "Web Development",
  "company": "Zdminds",
  "companyUrl": "https://www.linkedin.com/company/zdmindsindia/?viewAsMember=true",
  "location": "Work from home",
  "isRemote": true,
  "stipend": "₹ 10,000 - 20,000 /month",
  "stipendMin": 10000
}

{
  "listingId": "3150096",
  "listingType": "internships",
  "url": "https://internshala.com/internship/detail/work-from-home-python-development-internship-at-zdminds1778824954",
  "title": "Python Development",
  "company": "Zdminds",
  "companyUrl": "https://www.linkedin.com/company/zdmindsindia/?viewAsMember=true",
  "location": "Work from home",
  "isRemote": true,
  "stipend": "₹ 10,000 - 20,000 /month",
  "stipendMin": 10000
}

When you look at a couple of records side by side the analytical surface area opens up. The categorical fields invite grouping. The numeric fields invite ranking and distribution analysis. The timestamps invite time-series breakdowns. The text fields invite NLP.

Three things you can actually do with this

Build a leaderboard. Pick a numeric field, group by a categorical field, sort. Trivial in SQL or Pandas, surprisingly useful for tracking hiring trends, building talent pipelines, salary benchmarking and competitive recruiting intelligence.
Detect shifts over time. Snapshot the dataset daily, compute simple deltas between snapshots, alert on anything that moves more than a sensible threshold.
Cluster the long tail. The categorical fields probably have a power-law distribution. The long tail is often where the interesting outliers live -- the new entrants, the niche players, the anomalies.

Why it is not just "another scrape"

The reason this dataset is more interesting than typical scrape output: the source has organic structure. The fields are not invented by the scraper, they reflect how the underlying domain organises itself. That gives the dataset a kind of semantic coherence that synthetic or heavily-derived datasets lack.

Caveats

Sample sizes from a one-off run will not let you do anything statistically serious -- you want a longitudinal feed.
Some optional fields are sparsely populated; check density before relying on them.
The source can change. Treat any production pipeline as something that will need maintenance.

How I would prove the analytical thesis

If I were trying to justify investing engineering time in this dataset for a real project, the path would be: pull a one-week recurring sample to get past the snapshot bias, run the three analytical patterns above on the larger pull, and judge whether the conclusions hold up. If you can get a single non-obvious insight out of that exercise, the dataset is worth keeping. If everything you find is something you already knew, it probably is not -- find a different feed. That bar sounds harsh, but it saves you from a portfolio of datasets that nobody actually queries.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/internshala-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Why Imot.bg Bulgaria Real Estate data is more interesting than you would think

Can Yılmaz — Fri, 15 May 2026 13:11:07 +0000

On the surface, Imot.bg Bulgaria Real Estate sounds like the kind of dataset you would file under "boring infrastructure data" -- the sort of thing that lives in a corner of a warehouse and gets queried twice a quarter. After spending a bit of time actually looking at it, I have changed my mind. Here is why.

What is in it

Imot.bg Scraper Bulgaria Real Estate Listings to JSON, CSV & Excel Scrape property listings from imot.bg, Bulgaria's #1 real estate portal, into a clean, structured dataset. Each record carries a fairly rich set of fields:

listingId -- listing id
listingUrl -- listing url
title -- title
titleBg -- title bg
listingType -- listing type
propertyType -- property type
price -- price
priceCurrency -- price currency
priceFormatted -- price formatted
pricePerSqm -- price per sqm
area -- area
rooms -- rooms
floor -- floor
totalFloors -- total floors
constructionType -- construction type
yearBuilt -- year built
city -- city
cityBg -- city bg
neighborhood -- neighborhood
neighborhoodBg -- neighborhood bg
address -- address
description -- description
descriptionBg -- description bg
agencyName -- agency name
agencyPhone -- agency phone
agencyUrl -- agency url
isPrivateSeller -- is private seller
imageUrls -- image urls
imageThumbnail -- image thumbnail
publishedDate -- published date
scrapedAt -- scraped at

Two records from a sample run

{
  "listingId": "1b176062698062510",
  "listingUrl": "https://www.imot.bg/obiava-1b176062698062510-prodava-dvustaen-apartament-grad-plovdiv-ostromila",
  "title": "Продава 2-СТАЕН",
  "titleBg": "Продава 2-СТАЕН",
  "listingType": "sale",
  "propertyType": "apartment",
  "price": 110000,
  "priceCurrency": "EUR",
  "priceFormatted": "110,000 EUR",
  "pricePerSqm": 1375
}

{
  "listingId": "1b177874323496598",
  "listingUrl": "https://www.imot.bg/obiava-1b177874323496598-prodava-dvustaen-apartament-grad-sofiya-belite-brezi-ul-nishava",
  "title": "Продава 2-СТАЕН",
  "titleBg": "Продава 2-СТАЕН",
  "listingType": "sale",
  "propertyType": "apartment",
  "price": 234900,
  "priceCurrency": "EUR",
  "priceFormatted": "234,900 EUR",
  "pricePerSqm": 3051
}

Three things you can actually do with this

Build a leaderboard. Pick a numeric field, group by a categorical field, sort. Trivial in SQL or Pandas, surprisingly useful for rental yield analysis, neighbourhood pricing trends, investor due-diligence and market-timing models.
Detect shifts over time. Snapshot the dataset daily, compute simple deltas between snapshots, alert on anything that moves more than a sensible threshold.
Cluster the long tail. The categorical fields probably have a power-law distribution. The long tail is often where the interesting outliers live -- the new entrants, the niche players, the anomalies.

Why it is not just "another scrape"

Caveats

Sample sizes from a one-off run will not let you do anything statistically serious -- you want a longitudinal feed.
Some optional fields are sparsely populated; check density before relying on them.
The source can change. Treat any production pipeline as something that will need maintenance.

How I would prove the analytical thesis

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/imot-bg-scraper-bulgaria-real-estate. It supports JSON, CSV and Excel exports and runs on a schedule.

What I learned scraping Hirist.tech IT Jobs: schema, gotchas and the tooling that worked

Can Yılmaz — Fri, 15 May 2026 13:05:59 +0000

I had a short window this week to evaluate Hirist.tech IT Jobs as a data source. Here is the condensed write-up of what the data looks like, what surprised me, and the bits of infrastructure that paid off.

The source

Hirist.tech IT Jobs Scraper Scrape India Tech Job Listings, Salary & Skills Data Scrape IT and tech job listings from Hirist.tech India's #1 niche tech job portal with 4M+ registered professionals and 50K+ active listings. The relevant questions for any new source are always: is the markup stable, is pagination sensible, and how aggressively does it rate-limit. For this one, all three answers are "good enough that you can build on it" -- which is honestly more than I can say for a lot of supposedly easy targets.

The schema

What you get back per record:

jobId -- job id
url -- url
title -- title
company -- company
companyType -- company type
location -- location
isRemote -- is remote
salaryMin -- salary min
salaryMax -- salary max
salaryRaw -- salary raw
experienceMin -- experience min
experienceMax -- experience max
experienceRaw -- experience raw
skills -- skills
description -- description
recruiterName -- recruiter name
postedAt -- posted at
keyword -- keyword
scrapedAt -- scraped at

Nothing exotic, which is exactly what you want from a feed. Flat records, predictable keys, types you can guess from the names.

Real rows

Two records from a sample run, trimmed for the inevitable wall of text:

{
  "jobId": "1633448",
  "url": "https://www.hirist.tech/j/senior-data-engineer-1633448",
  "title": "Senior Data Engineer",
  "company": "Unico Talent",
  "companyType": null,
  "location": "Bangalore",
  "isRemote": false,
  "salaryMin": null,
  "salaryMax": null,
  "salaryRaw": null
}

{
  "jobId": "1633452",
  "url": "https://www.hirist.tech/j/software-engineer-fleet-management-1633452",
  "title": "Software Engineer - Fleet Management",
  "company": "PeopleWiz Consulting LLP",
  "companyType": null,
  "location": "Bangalore",
  "isRemote": false,
  "salaryMin": null,
  "salaryMax": null,
  "salaryRaw": null
}

Gotchas

A few things I would not have known without actually pulling data:

Optional fields disappear instead of being null. Not the end of the world, but it means every loader needs to be tolerant of missing keys.
Long-form text fields contain control characters. Newlines, tabs, the occasional rogue carriage return. Strip them at load time unless you actively want them.
Timestamps are UTC ISO-8601 which is great, but it does mean any local-time dashboard needs an explicit conversion.
Some numeric fields are emitted as strings. Cast on load.
Re-scraping with overlapping windows creates duplicates. Dedup on the natural ID.

What I would build next

A few directions this dataset would support nicely:

A daily snapshot pipeline that lands raw JSON into object storage, then materialises a curated table for dashboards.
A change-detection layer that computes row-level diffs between consecutive scrapes -- great for surfacing new and removed records.
A text-extraction layer over the long-form content fields, feeding into search or topic modelling.
A small validation suite that runs after every scrape: row count above a floor, key fields present in 100% of rows, timestamp parses cleanly. Cheap to write, catches schema drift in minutes instead of weeks.

Cost considerations

Bottom line

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/hirist-tech-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Scraping Himalayas Remote Jobs for recruiters: what data is available and how to use it

Can Yılmaz — Fri, 15 May 2026 13:00:41 +0000

If you are working in the recruiters space and you have ever needed Himalayas Remote Jobs as a structured feed, you know the gap between "the data exists on a website" and "the data is in my notebook" can swallow a whole sprint. Here is what the dataset actually contains and the workflow I would build around it.

Why this data matters for recruiters

The short version: tracking hiring trends, building talent pipelines, salary benchmarking and competitive recruiting intelligence. Himalayas Remote Jobs Scraper 100,000+ Remote Jobs Worldwide Scrape remote job listings from Himalayas (himalayas.app), one of the largest remote-work job boards with 100,000+ remote jobs, straight from its public API. For recruiters, talent-intel analysts and job-market researchers, the value is having a normalised, queryable representation of a source that ordinarily fights structured access.

Fields available

The dataset comes back with these fields per record:

title -- title
company -- company
companySlug -- company slug
companyLogo -- company logo
employmentType -- employment type
seniority -- seniority
categories -- categories
parentCategories -- parent categories
minSalary -- min salary
maxSalary -- max salary
currency -- currency
locationRestrictions -- location restrictions
timezoneRestrictions -- timezone restrictions
excerpt -- excerpt
description -- description
url -- url
postedAt -- posted at
expiresAt -- expires at
guid -- guid
scrapedAt -- scraped at

The mix is decent. You get enough identifying information to deduplicate across runs, enough content to actually answer questions, and enough timestamps to do time-series work.

Two example records

Trimmed for readability:

{
  "title": "Business Development Manager – Enterprise Team",
  "company": "KnowledgeBrief",
  "companySlug": "knowledgebrief",
  "companyLogo": "https://cdn-images.himalayas.app/htk59y2g3qaksdcowvhv1elbhata",
  "employmentType": "Full Time",
  "seniority": [
    "Manager"
  ],
  "categories": [
    "Enterprise-Business-Development-Manager",
    "Enterprise-Sales-Development-Manager",
    "... (2 more)"
  ],
  "parentCategories": [
    "Sales"
  ],
  "minSalary": 30000,
  "maxSalary": 40000
}

{
  "title": "Biologist with Python Experience - Freelance AI Trainer",
  "company": "Mindrift",
  "companySlug": "mindrift",
  "companyLogo": "https://cdn-images.himalayas.app/xq3hn9b4xx58golfhgf8twc4izd7",
  "employmentType": "Contractor",
  "seniority": [
    "Mid-level"
  ],
  "categories": [
    "AI-Training-Data-Creation",
    "Computational-Biology",
    "... (3 more)"
  ],
  "parentCategories": [],
  "minSalary": 158080,
  "maxSalary": 158080
}

A recruiter could start asking real questions on day one with this shape: aggregate counts across categorical fields, distributions on numeric fields, simple text analysis on the long-form content.

A workflow that works

If I were dropping this into an existing recruiters stack:

Schedule a recurring scrape. Daily or every few hours depending on how fast the source updates.
Land it raw. Object storage, partitioned by date. Cheap, replayable, future-proof against schema changes.
Curate. Dedup on the natural key, type-cast the columns, surface the curated view to your dashboard or notebook layer.
Layer enrichment. Most recruiters workflows need a second source -- reference data, internal CRM, third-party signal -- to extract real value. Build that join early.

Honest trade-offs

This is not a magic dataset. Things to know up-front:

The source can rate-limit you. Plan for retries and back-off.
Free-text fields are noisy. Budget for cleaning.
Schema can drift if the source redesigns. Wire up assertions on record counts and key presence.

Concrete questions you could answer day one

A recruiter working with this dataset could, on the first day:

Rank entities by any numeric field, broken down by a categorical field, to find leaders and laggards.
Build a time-series of new entries per day from the timestamp columns to see growth or decline.
Pull the long-form text into a quick TF-IDF or topic-model to surface what the dataset is actually about under the hood.
Spot duplicates and near-duplicates as a data-quality exercise, which often surfaces interesting structural anomalies in the source.

None of those questions require a finished pipeline. A notebook, the JSON file, and an afternoon are enough.

Verdict

For recruiters, this is a useful input -- not a finished answer, but a strong starting point that saves you from writing a brittle HTML parser of your own. The marginal cost of trying it on a real project is a few hours; the marginal value if the dataset clicks with your workflow is open-ended.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/himalayas-remote-jobs-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Comparing approaches to extracting Hacker News Who Is Hiring data

Can Yılmaz — Fri, 15 May 2026 12:55:35 +0000

There is more than one way to get Hacker News Who Is Hiring into a structured dataset, and the right answer depends a lot on how often you need fresh data, how much volume you are after, and how much engineering time you want to spend on the plumbing. Here is the trade-off matrix I worked through before settling on an approach.

What the data looks like (regardless of approach)

Hacker News Who Is Hiring Scraper Jobs, Salary & Tech Stack Data Scrape structured job listings from Hacker News "Ask HN: Who is Hiring?" monthly threads. The end-state schema is more or less fixed by the source:

commentId -- comment id
threadId -- thread id
threadTitle -- thread title
threadMonth -- thread month
author -- author
company -- company
role -- role
location -- location
remote -- remote
salary -- salary
techStack -- tech stack
visa -- visa
applyUrl -- apply url
email -- email
fullText -- full text
postedAt -- posted at
hnUrl -- hn url
scrapedAt -- scraped at

The differences between approaches are not really about schema -- they are about reliability, maintenance burden, and total cost of ownership.

Approach 1: Roll your own scraper

The DIY path. Pros: total control, no third-party dependency, very cheap on small volumes. Cons: you own the proxy rotation, the rate-limit handling, the retry logic, the schema-drift detection, the scheduling, the monitoring, and the bug pager.

If you have one engineer who has done this kind of work before and you only need one source, this is fine. If you need ten sources, the maintenance burden compounds faster than you would expect.

Approach 2: Generic crawl framework + custom selectors

The middle path. Use Scrapy or Playwright with your own parsing logic. Pros: less boilerplate, decent observability for free. Cons: you still own the proxy and rate-limit story, plus you are now coupled to a framework that has its own learning curve.

This is a sensible choice for multi-source projects where you want one mental model across all the scrapers.

Approach 3: Managed scraping infrastructure

Use a hosted runner that handles proxies, scheduling and storage. Pros: minimal engineering time, predictable cost, very fast to get a first run out the door. Cons: cost scales with volume, less control over edge cases.

For one-off explorations and steady-state recurring pipelines under a few million records per month, this is what I keep ending up on.

Two sample records (for context)

What the eventual output looks like, regardless of how you got there:

{
  "commentId": "47975574",
  "threadId": "47975571",
  "threadTitle": "Ask HN: Who is hiring? (May 2026)",
  "threadMonth": "May 2026",
  "author": "chrisposhka",
  "company": "Pathos AI",
  "role": "Senior Software",
  "location": "NYC",
  "remote": "Hybrid",
  "salary": null
}

{
  "commentId": "47975581",
  "threadId": "47975571",
  "threadTitle": "Ask HN: Who is hiring? (May 2026)",
  "threadMonth": "May 2026",
  "author": "verobytes",
  "company": "NetBird",
  "role": "Berlin, Germany",
  "location": "Berlin, Remote, remote",
  "remote": "Remote",
  "salary": null
}

How I would pick

A rough decision tree:

One-off exploration: managed approach. The setup-cost of DIY is not worth it for a single run.
Steady recurring feed, single source, modest volume: managed approach unless cost becomes prohibitive.
Multiple sources, large volume, dedicated team: framework + custom selectors. The unit economics flip.
Adversarial source with active anti-bot defences: probably a specialist provider or a custom build with serious proxy budget.

Verdict

For Hacker News Who Is Hiring specifically the volume and update-frequency profile is moderate, and a managed runner is the most defensible default. The dataset shape above is the same either way.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/hacker-news-who-is-hiring-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Sample dataset analysis: a 20-row snapshot of Google Ads Transparency Center

Can Yılmaz — Fri, 15 May 2026 12:50:18 +0000

I pulled a 20-row sample of Google Ads Transparency Center to see whether the dataset is rich enough to support outbound prospecting, ICP enrichment, account research and territory planning, or whether it is the kind of feed you have to enrich heavily before it becomes useful. Short answer: richer than I expected. Long answer below.

What is in the sample

Google Ads Transparency Center Scraper Competitor Ads, Impressions & Spend Scrape the Google Ads Transparency Center at scale and extract every Google ad your competitors are running across Search, Display, Shopping, and YouTube. Each record has the following fields:

adId -- ad id
advertiserId -- advertiser id
advertiserName -- advertiser name
advertiserDomain -- advertiser domain
format -- format
surface -- surface
imageUrl -- image url
imageWidth -- image width
imageHeight -- image height
imageHtml -- image html
iframeUrl -- iframe url
previewUrl -- preview url
variationCount -- variation count
firstShown -- first shown
lastShown -- last shown
variantUrls -- variant urls
targetingCategory -- targeting category
impressionsRange -- impressions range
impressionsRegions -- impressions regions
spendRange -- spend range
firstShownDetailed -- first shown detailed
lastShownDetailed -- last shown detailed
payer -- payer
detailFormatCode -- detail format code
searchedDomain -- searched domain
searchedAdvertiser -- searched advertiser
searchedRegions -- searched regions
searchedFormat -- searched format
scrapedAt -- scraped at
advertiserTotalAdsMin -- advertiser total ads min
advertiserTotalAdsMax -- advertiser total ads max

Two example records

Here are two rows from the sample, trimmed slightly so they fit:

{
  "adId": "CR17484233965576388609",
  "advertiserId": "AR16735076323512287233",
  "advertiserName": "Nike, Inc.",
  "advertiserDomain": "nike.com",
  "format": "IMAGE",
  "surface": "SEARCH",
  "imageUrl": "https://tpc.googlesyndication.com/archive/simgad/17926873754417759183",
  "imageWidth": 380,
  "imageHeight": 199,
  "imageHtml": "<img src=\"https://tpc.googlesyndication.com/archive/simgad/17926873754417759183\" height=\"199\" width=\"380\">"
}

{
  "adId": "CR02684696164518854657",
  "advertiserId": "AR16832577870747402241",
  "advertiserName": "NIKE GLOBAL TRADING B.V. SINGAPORE BRANCH",
  "advertiserDomain": "nike.com",
  "format": "DISPLAY",
  "surface": "SHOPPING",
  "imageUrl": null,
  "imageWidth": null,
  "imageHeight": null,
  "imageHtml": null
}

Even without aggregation you can see the cardinality is interesting. The descriptive fields vary widely across rows, which means a 20-row sample is enough to do meaningful exploratory analysis but probably not enough for any production-grade modelling -- you would want at least an order of magnitude more.

What I would do with the data

A non-exhaustive list of analyses this dataset directly supports:

Frequency analysis on the categorical columns to spot dominant clusters and long-tail outliers.
Time-series breakdowns using the timestamp fields to see daily, weekly and seasonal patterns.
Text analysis on the free-form fields -- topic modelling, keyword extraction, sentiment if the content warrants it.
Cross-joins with external reference data (outbound prospecting, ICP enrichment, account research and territory planning typically needs a second-source enrichment step) to produce something more valuable than either input alone.

Quirks I noticed

A few practical observations from poking at the rows:

Some optional fields are missing rather than null. Normalise on load.
Long-form text occasionally contains newlines and the odd unicode quirk; clean before tokenising.
Identifier-like fields are strings; do not let your warehouse coerce them to int.

How I would shape it for downstream use

Bottom line

For a sample pull it is more than enough to validate the use-case fit. If the analytical questions you want to answer are reasonable on a 20-row sample, the full dataset will comfortably answer them. The next step is a longer-horizon pull -- a week or two of recurring snapshots -- which lets you stop treating each row as a one-off and start treating the dataset as a feed with its own dynamics.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/google-ads-transparency-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

Comparing approaches to extracting Finn.No data

Can Yılmaz — Fri, 15 May 2026 12:44:59 +0000

There is more than one way to get Finn.No into a structured dataset, and the right answer depends a lot on how often you need fresh data, how much volume you are after, and how much engineering time you want to spend on the plumbing. Here is the trade-off matrix I worked through before settling on an approach.

What the data looks like (regardless of approach)

Finn.no Scraper Real Estate, Cars, Jobs & Marketplace Data for Norway Scrape Finn.no Norway's largest classifieds platform and export structured listing data to JSON, CSV or Excel. The end-state schema is more or less fixed by the source:

finnkode -- finnkode
url -- url
adType -- ad type
title -- title
location -- location
localAreaName -- local area name
price -- price
totalPrice -- total price
monthlyFee -- monthly fee
size -- size
plotSize -- plot size
ownershipType -- ownership type
propertyType -- property type
bedrooms -- bedrooms
viewingDate -- viewing date
agent -- agent
agentLogoUrl -- agent logo url
imageUrl -- image url
imageUrls -- image urls
lat -- lat
lng -- lng
scrapedAt -- scraped at

The differences between approaches are not really about schema -- they are about reliability, maintenance burden, and total cost of ownership.

Approach 1: Roll your own scraper

If you have one engineer who has done this kind of work before and you only need one source, this is fine. If you need ten sources, the maintenance burden compounds faster than you would expect.

Approach 2: Generic crawl framework + custom selectors

This is a sensible choice for multi-source projects where you want one mental model across all the scrapers.

Approach 3: Managed scraping infrastructure

For one-off explorations and steady-state recurring pipelines under a few million records per month, this is what I keep ending up on.

Two sample records (for context)

What the eventual output looks like, regardless of how you got there:

{
  "finnkode": "463621591",
  "url": "https://www.finn.no/realestate/homes/ad.html?finnkode=463621591",
  "adType": "realestate",
  "title": "Innbydende og oppgradert 3-roms leilighet | V.v & fyring inkl. | Epoq kjøkken | Innglasset balkong | Ingen forkjøpsrett!",
  "location": "Nordtvetbakken 2, Oslo",
  "localAreaName": "KALBAKKEN",
  "price": "4 300 000 kr",
  "totalPrice": "4 403 798 kr",
  "monthlyFee": "6 884 kr",
  "size": "70 m²"
}

{
  "finnkode": "463301345",
  "url": "https://www.finn.no/realestate/homes/ad.html?finnkode=463301345",
  "adType": "realestate",
  "title": "Strøken 3-roms hjørneleilighet fra 2023 med sørvestvendt innglasset balkong og eget vaskerom | P-plass i kjeller og heis",
  "location": "Melhustunet 24B, Melhus",
  "localAreaName": "MELHUS SENTRUM",
  "price": "5 990 000 kr",
  "totalPrice": "6 140 840 kr",
  "monthlyFee": "2 680 kr",
  "size": "83 m²"
}

How I would pick

A rough decision tree:

One-off exploration: managed approach. The setup-cost of DIY is not worth it for a single run.
Steady recurring feed, single source, modest volume: managed approach unless cost becomes prohibitive.
Multiple sources, large volume, dedicated team: framework + custom selectors. The unit economics flip.
Adversarial source with active anti-bot defences: probably a specialist provider or a custom build with serious proxy budget.

Verdict

For Finn.No specifically the volume and update-frequency profile is moderate, and a managed runner is the most defensible default. The dataset shape above is the same either way.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/finn-no-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

A field-by-field look at Dev.to Articles data: structure, types and edge cases

Can Yılmaz — Fri, 15 May 2026 12:39:38 +0000

When you are evaluating a new data source the first thing you want is not the marketing pitch, it is the schema. Here is a field-by-field walkthrough of what Dev.to Articles actually returns, based on a sample I pulled while researching the source.

What this dataset is

Dev.to Articles Scraper Developer Blog Posts by Tag or Author to JSON/CSV Scrape developer articles from Dev.to straight from its official public API pull posts by tag or author with full metadata and paginate the entire feed. In practice that means each record is one logical entity -- one listing, one post, one record, depending on the source -- with all of the fields you would expect plus a few metadata columns added by the scraper.

The fields

id -- id
title -- title
description -- description
url -- url
author -- author
authorUsername -- author username
tags -- tags
commentsCount -- comments count
reactionsCount -- reactions count
readingTimeMinutes -- reading time minutes
coverImage -- cover image
publishedAt -- published at
scrapedAt -- scraped at

A quick read on each category:

Identifiers are stable across re-scrapes and safe to use as natural keys. They are strings even if they look numeric.
Content fields are the actual payload. Expect free-form text, some HTML residue if the source had any, and the occasional non-ASCII character.
Numeric fields (counts, prices, scores) tend to be already-coerced to int or float -- but always double-check the first run because some sources emit them as strings.
Timestamps come back as ISO-8601 UTC, which is the right default.
Provenance fields like a scrapedAt or source URL tell you when and where the row came from. Keep them in your warehouse for audit purposes.

Two real rows

Here is what two trimmed records look like:

{
  "id": 3666204,
  "title": "4 Tiny Mistakes That Secretly Destroy App Performance",
  "description": "Ok, I’m back from my short vacation and returning with some useful content 😄 As you know, from time...",
  "url": "https://dev.to/sylwia-lask/4-tiny-mistakes-that-secretly-destroy-app-performance-3cgo",
  "author": "Sylwia Laskowska",
  "authorUsername": "sylwia-lask",
  "tags": [
    "javascript",
    "angular",
    "... (2 more)"
  ],
  "commentsCount": 25,
  "reactionsCount": 35,
  "readingTimeMinutes": 6
}

{
  "id": 3661749,
  "title": "React is Overkill: Why Python + HTMX is Dominating in 2026",
  "description": "Last year I spent forty minutes setting up a React project for an internal admin dashboard. Just the...",
  "url": "https://dev.to/syedahmershah/react-is-overkill-why-python-htmx-is-dominating-in-2026-17ib",
  "author": "Syed Ahmer Shah",
  "authorUsername": "syedahmershah",
  "tags": [
    "python",
    "react",
    "... (2 more)"
  ],
  "commentsCount": 66,
  "reactionsCount": 155,
  "readingTimeMinutes": 8
}

Edge cases to plan for

Three patterns I saw that you should pre-empt in your loader:

Missing optional keys. Some rows have a field that other rows do not. Always use .get() semantics, never positional access.
Encoding artefacts in text columns. UTF-8 throughout the pipeline. If you have a Windows-1252 layer anywhere, expect smart quotes to break it.
Duplicate rows across overlapping runs. If you scrape every six hours you will see overlap. Dedup on the natural identifier.

How I would model it in a warehouse

The natural shape for a destination table is one row per source entity, with the identifier promoted to a primary key and the timestamp columns cast to TIMESTAMP. Free-text columns go into a TEXT/VARCHAR(MAX) and any list-shaped values either get exploded into a child table or stored as a JSON column depending on whether you need to query the elements individually.

A typical loader for this shape might look like: read the raw JSON into a DataFrame with pd.json_normalize, apply a small column-rename map, write to a staging table with to_sql or your warehouse's bulk loader, then run a MERGE statement keyed on the natural identifier into the curated table. The whole pipeline is comfortably under a hundred lines of code if you do not over-engineer it.

Who this is for

Community managers, trend researchers and brand-monitoring teams are the natural audience. The dataset is rich enough to support real analytical questions but flat enough to land in a warehouse with one statement. If you are evaluating sources for a new project, this is the kind of dataset where the cost-benefit is firmly on the "just use it" side -- the engineering work to integrate is small relative to the analytical value you get out.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/devto-articles-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.