We extract objective electronics test data, review scores, raw measurements, and methodology metrics from Rtings. 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 TV Reviews objects from rtings.com. All fields typed and schema-versioned.
"review_id": "lg-c3-oled", "product_name": "LG C3 OLED", "brand": "LG", "panel_type": "WOLED", "overall_score": 8.9, "gaming_score": 9.3, "contrast_ratio": "Inf:1", "peak_brightness_hdr": 820
| # | review_id | product_name | brand | resolution | panel_type | overall_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Headphone Reviews objects from rtings.com. All fields typed and schema-versioned.
"review_id": "sony-wh-1000xm5", "product_name": "Sony WH-1000XM5 Wireless", "brand": "Sony", "anc_supported": true, "overall_score": 8.1, "neutral_sound_score": 7.4, "commute_travel_score": 8.5, "noise_isolation": 9.0
| # | review_id | product_name | brand | enclosure_type | anc_supported | overall_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Monitor Reviews objects from rtings.com. All fields typed and schema-versioned.
"review_id": "dell-alienware-aw3423dw", "product_name": "Dell Alienware AW3423DW", "brand": "Dell", "refresh_rate": 175, "panel_type": "QD-OLED", "gaming_score": 9.2, "input_lag_60hz": 14.1, "color_gamut_srgb": 100.0
| # | review_id | product_name | brand | size_inches | resolution | refresh_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Raw Measurements objects from rtings.com. All fields typed and schema-versioned.
"product_id": "lg-c3-oled", "category": "TVs", "metric_name": "Input Lag (4k @ 120Hz)", "metric_value": 5.4, "metric_unit": "ms", "test_methodology_version": "v1.2", "sub_score": 9.8, "test_date": "2023-04-12"
| # | product_id | category | metric_name | metric_value | metric_unit | test_methodology_version |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Comparisons objects from rtings.com. All fields typed and schema-versioned.
"comparison_id": "lg-c3-oled-vs-samsung-s90c-oled", "product_a_id": "lg-c3-oled", "product_b_id": "samsung-s90c-oled", "category": "TVs", "winner_overall": "samsung-s90c-oled", "score_diff_overall": 0.1, "winner_gaming": "Tie", "score_diff_gaming": 0.0
| # | comparison_id | product_a_id | product_b_id | category | winner_overall | score_diff_overall |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Rtings scraper parses complex methodology tables, extracts raw measurements from interactive charts, and normalises scores across different TestBench versions.
Extract overall scores, usage ratings, and detailed breakdown scores for TVs, monitors, headphones, and more.
Capture contrast ratios, input lag milliseconds, color dE values, and frequency response metrics from test results.
Track which test methodology version applies to each score to ensure longitudinal data consistency.
Extract side-by-side comparison matrices and auto-generated winner verdicts across all product categories.
Execute JavaScript to render and extract data points from interactive frequency response and color gamut charts.
Monitor score adjustments when Rtings updates their scoring formulas or test methodologies.
Extract panel types, firmware versions tested, release years, and physical dimensions.
Identify which reviews are currently locked behind the Insider paywall for future extraction scheduling.
Run weekly pipelines to capture newly published reviews and updated comparison verdicts.
Brief in. Clean data out.
Specify product categories, target metrics, or specific comparison pairs. We design the extraction schema.
We configure Scrapy and Playwright crawlers to handle Rtings dynamic charts and tabbed test results.
Schema validation, unit checks on raw measurements, and missing-data detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Rtings requires extensive browser automation to expose hidden tabs and render interactive data visualisations.
Rtings uses complex interactive charts for frequency response and color gamut. We run full headless browsers to execute the rendering logic and extract the underlying JSON data points.
Test results are hidden behind interactive tabs. Our crawlers simulate user clicks to expose all sub-scores and raw measurements without missing hidden DOM nodes.
Rtings frequently updates their TestBench methodology, which changes existing scores. We track the methodology version alongside every metric to prevent data corruption in your warehouse.
We use residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass basic scraping protections and rate limits.
Extracting all possible product comparisons requires traversing thousands of URL permutations. We manage the queue efficiently to capture all side-by-side verdicts.
Hardware manufacturers track how their products score against competitors in objective measurements like input lag and color accuracy.
eCommerce stores ingest objective sub-scores to power advanced filtering on their own storefronts.
Product managers analyze historical test data to identify gaps in the market, such as the lack of high-refresh-rate IPS monitors in specific price brackets.
ML teams use structured objective measurements and review verdicts to train product recommendation engines and LLMs.
Publishers use raw comparison data to automate the generation of quantitative buying guides.
Brands correlate objective performance scores with retail prices to optimize product positioning and promotional discounts.
"Rtings possesses the most rigorous objective electronics test data available, but accessing the raw measurements requires navigating complex interactive charts and dynamic DOM structures."
Most teams struggle to extract the raw data points from Rtings because the most valuable metrics are buried in JavaScript-rendered charts and interactive tabs. DataFlirt handles the browser automation, methodology version tracking, and schema normalisation so your data science teams get clean, queryable measurements.
Everything supported by our rtings.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 and deduplication. Playwright handles JavaScript rendering for interactive charts and tabbed test results.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions to bypass rate limits smoothly.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About rtings.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available, factual test data is generally permissible. DataFlirt targets only public reviews and measurements. We do not bypass the Insider paywall or extract copyrighted editorial text verbatim. Clients should consult legal counsel for specific use cases.
Yes. Our Playwright crawlers execute the JavaScript required to render frequency response, color gamut, and other interactive charts, extracting the underlying data points directly.
Rtings frequently updates their TestBench versions, which alters historical scores. We capture the methodology version alongside every metric, allowing you to filter or normalise data across different test versions.
Yes. We can extract the side-by-side matrices, score differentials, and categorical winners for any combination of products.
Absolutely. We extract the raw values (e.g., 14.2 ms input lag, 850 nits peak brightness) alongside the normalized 1-10 scores.
Pipelines can be configured to run daily or weekly to capture newly published reviews, updated comparisons, and methodology recalculations.
No. We respect authentication walls. Reviews are extracted only once they become publicly available on the platform.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of all TV measurements or a continuous feed of new hardware reviews, we build and operate the pipeline. Tell us what you need.
