We extract articles, product reviews, author metrics, and comment sentiment from 9to5Mac. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Articles & News objects from 9to5mac.com. All fields typed and schema-versioned.
"url": "https://9to5mac.com/2026/04/12/iphone-18-pro-rumours/", "headline": "iPhone 18 Pro to feature under-display Face ID", "author": "Chance Miller", "pub_date": "2026-04-12T14:30:00Z", "category": "iPhone", "tags": "['iPhone 18', 'Face ID', 'Rumours']", "word_count": 842
| # | url | headline | author | pub_date | updated_date | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Reviews objects from 9to5mac.com. All fields typed and schema-versioned.
"product_name": "MacBook Pro M5 Max", "review_score": 4.5, "pros": "['Battery life', 'Display brightness']", "cons": "['Price', 'Weight']", "verdict": "The ultimate workstation for creative professionals.", "author": "Zac Hall", "pub_date": "2026-03-15T09:00:00Z"
| # | url | product_name | review_score | pros | cons | affiliate_links |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Author Profiles objects from 9to5mac.com. All fields typed and schema-versioned.
"author_id": "chance-miller", "name": "Chance Miller", "twitter_handle": "@ChanceHMiller", "article_count": 5241, "role": "Editor-in-Chief", "latest_article_url": "https://9to5mac.com/author/chance-miller/latest", "bio": "Chance covers the latest Apple news and rumours."
| # | author_id | name | bio | twitter_handle | article_count | latest_article_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Rumour Tracker objects from 9to5mac.com. All fields typed and schema-versioned.
"rumour_topic": "Apple Car Project Titan Revived", "expected_release": "2028-Q4", "probability_score": 35, "status": "Unconfirmed", "last_updated": "2026-02-10T11:20:00Z", "source_links": "['bloomberg.com/news/...']", "tags": "['Project Titan', 'Apple Car']"
| # | rumour_topic | expected_release | probability_score | source_links | related_articles | status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Comments & Engagement objects from 9to5mac.com. All fields typed and schema-versioned.
"article_url": "https://9to5mac.com/2026/04/12/iphone-18-pro-rumours/", "comment_id": "c_98237491", "username": "MacFan99", "comment_text": "Under-display Face ID is long overdue.", "upvotes": 142, "downvotes": 3, "timestamp": "2026-04-12T15:01:22Z"
| # | article_url | comment_id | username | comment_text | upvotes | downvotes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our 9to5Mac scraper targets the structural metadata of tech journalism: author timelines, affiliate link graphs, review verdicts, and comment sentiment. We manage the DOM complexity so you receive clean, structured feeds.
Extract headlines, body text, publication timestamps, and embedded media across all categories.
Track publication frequency, bios, and social links for every contributing author.
Isolate review scores, structured pros/cons lists, and final verdicts from editorial content.
Extract and unshorten outbound affiliate URLs to map monetisation strategies.
Scrape embedded comment sections for user sentiment, upvotes, and discussion threads.
Map articles to specific Apple device categories, OS versions, and custom tags.
Identify and isolate specific leak claims, source attributions, and expected release dates.
Monitor RSS feeds and the homepage for breaking news with sub-minute latency.
Backfill years of Apple news by traversing the complete 9to5Mac publication archive.
Brief in. Clean data out.
Provide target categories, author feeds, or date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for 9to5mac.com.
Schema validation, null-rate checks, and article completeness verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Media sites deploy aggressive caching and varied DOM structures. Here is how we maintain stable extraction.
Media publishers rely heavily on CDNs like Cloudflare or Fastly. We utilise cache-busting headers and direct edge-node routing to ensure we capture article updates and breaking news the moment they are published.
Reviews, standard news, and live blogs use different WordPress templates. Our selectors employ fallback chains and semantic HTML parsing to extract core fields regardless of the specific page layout.
We execute JavaScript to trigger infinite scroll events and parse underlying REST API responses, ensuring complete extraction of historical archives without missing intermediate posts.
Comments are often loaded asynchronously via third-party platforms. We intercept the network requests to these APIs, extracting the raw JSON thread data rather than scraping the rendered DOM.
News articles are frequently updated post-publication. We maintain a hash index of article bodies and emit diffs when an article is modified, providing a transparent audit trail of editorial changes.
Tech publishers monitor 9to5Mac's publication velocity, author output, and topic coverage to benchmark their own editorial strategy.
Accessory manufacturers track product mentions, review scores, and brand sentiment across Apple-focused media.
Marketing teams extract outbound affiliate links to reverse-engineer monetisation strategies and partner networks.
Hedge funds and analysts mine comment sections and review verdicts to gauge consumer sentiment around new Apple product launches.
Supply chain analysts aggregate leak reports and expected release dates to model future component demand.
LLM developers use structured, high-quality tech journalism datasets to fine-tune models on domain-specific terminology.
"9to5Mac publishes the most critical Apple ecosystem updates, but turning unstructured tech journalism into queryable datasets requires dedicated infrastructure."
Extracting data from modern media publishers involves navigating aggressive CDN caching, infinite scroll pagination, and frequently changing WordPress DOM structures. DataFlirt manages these extraction complexities so your engineering team can focus on deriving insights from the Apple news cycle.
Everything supported by our 9to5mac.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, infinite scroll, and interaction flows.
We maintain pools of residential ISP proxies to bypass aggressive CDN rate limiting and bot protection rules.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About 9to5mac.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public news articles, reviews, and metadata. We do not circumvent authentication walls or extract proprietary internal data. Clients should review relevant terms of service.
We use residential ISP proxies and full Playwright browser sessions with realistic fingerprints. This ensures reliable access through standard CDN security challenges without triggering rate limits.
For breaking news monitoring, we can configure pipelines to poll RSS feeds and the homepage at sub-minute intervals. Full historical backfills are executed as batch processes.
Yes. We can traverse the complete pagination archive to extract articles dating back to the site's inception, providing a comprehensive historical dataset.
Yes. We extract threaded discussions, usernames, timestamps, and upvote/downvote metrics from the embedded comment platforms used on the site.
We deliver data in JSON, CSV, XLS, and Parquet formats. We can push directly to AWS S3, BigQuery, Snowflake, or send real-time updates via Webhook and API.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical archive of Apple news or a real-time feed of product reviews — we scope, build, and operate the pipeline. Tell us what you need.
