We extract deep-dive technical reviews, Bench database metrics, component specifications, and community forum threads from AnandTech. 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 Reviews & Articles objects from anandtech.com. All fields typed and schema-versioned.
"article_id": "98451", "url": "https://www.anandtech.com/show/98451/the-intel-core-i9-14900k-review", "title": "The Intel Core i9-14900K Review", "author": "Dr. Ian Cutress", "publish_date": "2023-10-17T13:00:00Z", "category": "CPUs", "page_count": 14, "comment_count": 342
| # | article_id | url | title | author | publish_date | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bench Database objects from anandtech.com. All fields typed and schema-versioned.
"component_name": "AMD Ryzen 9 7950X", "category": "CPU", "test_name": "Cinebench R23 Multi-Threaded", "score": 38412, "unit": "Points", "higher_is_better": true, "generation": "Zen 4"
| # | component_id | component_name | category | test_name | score | unit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Component Specs objects from anandtech.com. All fields typed and schema-versioned.
"product_name": "NVIDIA GeForce RTX 4090", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "process_node": "TSMC 4N", "tdp": "450W", "msrp": 1599.0, "memory_type": "24GB GDDR6X"
| # | product_name | manufacturer | architecture | process_node | transistor_count | tdp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Forum Threads objects from anandtech.com. All fields typed and schema-versioned.
"thread_id": "2598123", "forum_category": "CPUs and Overclocking", "title": "Raptor Lake Undervolting Results", "author": "Overclocker99", "view_count": 14502, "reply_count": 87, "is_sticky": false, "is_locked": false
| # | thread_id | forum_category | title | author | post_date | view_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Forum Posts objects from anandtech.com. All fields typed and schema-versioned.
"post_id": "40192834", "thread_id": "2598123", "author_username": "TechEnthusiast", "author_post_count": 4512, "post_body": "I managed to hit 5.8GHz all-core at 1.25V. Temperatures are stable at 82C under sustained load.", "timestamp": "2023-10-18T09:14:00Z", "hardware_signature": "i9-13900K | RTX 4090 | 64GB DDR5-6000"
| # | post_id | thread_id | author_username | author_join_date | author_post_count | post_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
AnandTech contains the most rigorous hardware benchmark data on the internet. We extract multi-page deep dives, normalise legacy benchmark tables, and parse nested forum quotes into structured schemas.
Extract entire component categories from the AnandTech Bench tool. Normalise test names, scores, and system configurations across CPU, GPU, and SSD categories.
Hardware reviews span 10 to 20 pages. We traverse pagination logic to stitch full articles, preserving section headers and conclusion summaries.
Convert complex HTML specification tables into flat JSON objects. Capture process nodes, transistor counts, TDP, and architectural details.
Extract community discussions from the AnandTech Forums. Capture post bodies, author metadata, hardware signatures, and nested quote hierarchies.
Parse inline JavaScript and HTML tables used to render performance charts, extracting raw benchmark figures rather than just image URLs.
Track output by specific technical editors. Extract publication frequency, covered categories, and historical article catalogues.
Extract reader comments on main site articles. Capture user sentiment, technical corrections, and community debate on hardware releases.
Extract specialized benchmark data for smartphone SoCs, battery life tests, and display calibration metrics.
Run bulk export pipelines to capture the entire historical archive of AnandTech reviews and forums for LLM training or archival.
Brief in. Clean data out.
Provide URLs for article categories, Bench tools, or forum sections. We design the extraction schema together.
We configure Scrapy crawlers, handle forum rate limits, and build custom parsers for legacy HTML table structures.
Schema validation, unit normalisation for benchmark scores, and pagination checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Extracting structured data from a 25-year-old site architecture requires specific handling for legacy markup and inconsistent table structures.
AnandTech's article templates have evolved over two decades, resulting in inconsistent HTML structures. Our selectors use multi-layer fallback chains to handle 2004-era table layouts just as reliably as modern div-based grids.
Benchmark units change over time. We apply regex and lookup tables to normalise units like 'FPS', 'Seconds', and 'Watts', ensuring your downstream database receives clean, typed numerical data.
Forum users frequently quote multiple previous posts. We parse BBCode and HTML blockquotes to maintain conversational hierarchy, allowing accurate NLP and sentiment analysis.
Both articles and forum threads use deep pagination. Our crawlers manage state across hundreds of pages per thread, ensuring zero dropped records during large historical exports.
To prevent IP bans and maintain pipeline stability, we strictly control concurrency and implement intelligent backoff strategies when accessing dense forum archives.
Analyse historical performance trends across CPU and GPU generations to map architectural improvements over time.
Ingest three decades of highly technical hardware reviews and forum discussions to train domain-specific language models.
Semiconductor companies extract Bench database metrics to compare their silicon against competitors across standardized workloads.
Extract forum threads and article comments to gauge enthusiast community reaction to new hardware launches and pricing.
Correlate historical MSRP data extracted from spec tables with benchmark performance to track price-to-performance ratios.
Create structured backups of invaluable technical journalism and community knowledge bases before legacy platforms degrade.
"AnandTech holds three decades of the most rigorous hardware benchmark data on the internet, but extracting it from legacy HTML tables requires a purpose-built pipeline."
Most teams underestimate the investment required. Extracting multi-page deep dives, normalising legacy benchmark tables, and parsing nested forum quotes demands specific selector strategies and rate-limit management. DataFlirt handles the extraction logic so your data science teams can focus on performance modelling.
Everything supported by our anandtech.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 high-throughput crawl orchestration, deduplication, and retry logic optimized for static HTML content.
We deploy specialized BeautifulSoup and lxml pipelines to normalise broken or legacy HTML structures found in older articles.
Pipelines run on Kubernetes clusters. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About anandtech.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available articles, benchmarks, and forum posts is generally permissible under applicable law for non-commercial research or internal analytics. DataFlirt extracts only public data and does not circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
Yes. Our parsers are built to handle the legacy HTML structures and inconsistent table formatting used in AnandTech's earliest archives.
We crawl the Bench tool by component category, extracting the underlying HTML tables to capture test names, scores, and system configurations, outputting them as a flat relational dataset.
We extract the high-resolution URLs for all embedded images and charts. We can also configure the pipeline to download the binary image files to your S3 bucket if required.
We provide the structured text data, author metadata, and timestamps required for your data science teams to run sentiment analysis and NLP models.
By default, we stitch paginated articles into a single JSON record. The 'post_body' field contains the full text, while page-specific metadata can be preserved in a nested array if requested.
Yes. We provide a sample run of up to 100 articles or 50 forum threads during the scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need the historical Bench database or a complete archive of the CPU forums, we scope, build, and operate the pipeline. Tell us what you need.
