We extract smartphone specs, laptop configurations, expert ratings, and price comparisons from digit.in. 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 Smartphone Specs objects from digit.in. All fields typed and schema-versioned.
"brand": "Samsung", "model": "Galaxy S24 Ultra", "digit_rating": 89, "processor": "Snapdragon 8 Gen 3", "ram_gb": 12, "battery_mah": 5000, "current_price": 129999.0
| # | device_id | brand | model | digit_rating | user_rating | processor |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Expert Reviews objects from digit.in. All fields typed and schema-versioned.
"title": "Samsung Galaxy S24 Ultra Review: The AI Phone", "author": "Digit Test Lab", "verdict": "The most complete Android flagship money can buy.", "pros": "['Brilliant display', 'Excellent battery life', 'Versatile cameras']", "cons": "['Expensive', 'Slow charging speed']", "digit_score": 89, "publish_date": "2024-01-25"
| # | review_id | device_id | title | author | publish_date | verdict |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Price Tracking objects from digit.in. All fields typed and schema-versioned.
"store_name": "Amazon", "price": 129999.0, "availability": "In Stock", "timestamp": "2024-05-12T10:30:00Z", "discount_pct": 5, "emi_options": true, "exchange_offer": true
| # | device_id | store_name | store_url | price | availability | timestamp |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Laptops & PCs objects from digit.in. All fields typed and schema-versioned.
"brand": "ASUS", "cpu_model": "Intel Core i9-13980HX", "gpu_model": "NVIDIA RTX 4090", "ram_gb": 32, "storage_gb": 2048, "price": 349990.0, "weight_kg": 2.5
| # | device_id | brand | series | cpu_model | gpu_model | ram_gb |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Zero1 Awards objects from digit.in. All fields typed and schema-versioned.
"award_year": 2023, "category": "Best Premium Smartphone", "winner_brand": "Apple", "winner_model": "iPhone 15 Pro Max", "runner_up_model": "Samsung Galaxy S23 Ultra", "best_buy_model": "Google Pixel 8 Pro", "winner_price": 159900.0
| # | award_year | category | winner_brand | winner_model | runner_up_model | best_buy_model |
|---|---|---|---|---|---|---|
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Our digit.in scraper handles varying specification schemas, paginated review archives, dynamic price widgets, and historical award data — normalising messy HTML into clean, queryable datasets.
Extract over 100 data points per device across categories: smartphones, laptops, audio, and wearables. Normalised fields for RAM, storage, and battery metrics.
Capture the overall Digit score alongside granular sub-scores for performance, design, features, and value for money.
Historical data on category winners, runners-up, and best buy recommendations from Digit's annual Zero1 awards.
Extract multi-store price comparisons embedded in device pages, including Amazon, Flipkart, and brand stores.
Parse structured pros, cons, final verdicts, and full review text from the Digit Test Lab and user submissions.
Extract published Geekbench, AnTuTu, PCMark, and 3DMark scores embedded within review articles.
Link RAM, storage, and colour variants to base models to maintain a clean product hierarchy.
Monitor launch dates, upcoming statuses, and discontinued flags for thousands of consumer electronics.
Run continuous pipelines to track price drops and new device additions, receiving only changed records.
Brief in. Clean data out.
Provide specific brands, device categories, or award years. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and normalisation rules for digit.in.
Schema validation, null-rate checks, and unit standardisation (e.g., standardising mAh and GB formats).
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting tech specs sounds simple until you encounter 50 different category templates. Here is how we ensure data consistency.
Smartphone spec tables look entirely different from laptop configurations. We map varying HTML structures into a unified, strongly-typed schema, ensuring 'RAM' is always an integer representing gigabytes, regardless of how it is formatted on the page.
Price comparisons on digit.in are often loaded asynchronously via JavaScript after page load. We use Playwright to wait for network idle states and API responses, capturing the exact multi-store pricing data.
Navigating years of reviews and discontinued products requires precise pagination logic. Our crawlers traverse category trees systematically, ensuring no device is missed, even if it is buried on page 400.
We strip HTML tags, normalise Unicode characters, and cast strings to appropriate data types. '5,000 mAh' becomes the integer 5000; 'Rs. 1,29,999' becomes the float 129999.0.
For ongoing pipelines, we maintain a state file of last-seen values. You only receive updates when a device's price drops or its Digit rating is revised, saving compute and storage costs.
Hardware brands track how their devices stack up against competitors in Digit Test Lab scores and benchmark results.
Aggregators feed their databases with updated multi-store pricing and availability status extracted from device pages.
Analysts track historical trends in RAM, storage, and battery capacities across price segments over multiple years.
Distributors and retailers use Zero1 award winners and top-rated devices to inform their purchasing decisions.
Machine learning teams use structured expert reviews and pros/cons lists to train sentiment analysis and recommendation models.
New tech blogs and affiliate sites enrich their internal catalogues with base specifications and launch data.
"Digit.in holds the most rigorous gadget testing data in India, but extracting normalised specs across 50 categories requires a dedicated pipeline."
Consumer electronics data is notoriously messy. Smartphone spec tables look entirely different from laptop configurations. DataFlirt normalises these varying schema structures, handles dynamic price widget loading, and delivers clean, queryable gadget intelligence directly to your warehouse.
Everything supported by our digit.in 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 dynamic price widgets and lazy-loaded spec tables.
Custom Python middleware cleans unstructured text, strips HTML, and enforces strict typing before records reach your warehouse.
Pipelines run on AWS Lambda and ECS. 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 digit.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from digit.in is generally permissible. DataFlirt targets only public, non-authenticated device specifications, reviews, and pricing data. We do not extract personal data or circumvent authentication walls.
We build category-specific normalisation rules. A smartphone's 'Display' field maps differently than a laptop's 'Display' field. Our pipeline standardises these variations into a unified schema before delivery.
Yes. We can extract historical winners, runners-up, and best buy recommendations across all categories for any given year.
For active price tracking pipelines, we can run daily or sub-daily cadences to capture price drops and availability changes across the embedded store links.
Yes. We parse review text and performance tables to extract specific benchmark scores like Geekbench, AnTuTu, and PCMark when published by the Digit Test Lab.
Absolutely. We provide a sample run of up to 500 devices as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of historical Zero1 awards or a continuous feed of smartphone specs and pricing — we build and operate the pipeline. Tell us what you need.
