We extract fan specifications, lighting lumen outputs, appliance pricing, and dealer network data from Orient Electric. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Lighting Data objects from orientelectric.com. All fields typed and schema-versioned.
"sku": "LED-BULB-9W-CDL", "title": "9W LED Bulb Cool Day Light", "wattage": "9W", "lumen_output": "900lm", "colour_temperature": "6500K", "base_type": "B22", "price": 120.0, "mrp": 150.0
| # | sku | title | category | wattage | lumen_output | colour_temperature |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fans & Air Delivery objects from orientelectric.com. All fields typed and schema-versioned.
"sku": "CEILING-AERO-1200", "title": "Aeroquiet Ceiling Fan 1200mm", "fan_type": "Ceiling", "sweep_size_mm": 1200, "rpm": 310, "air_delivery_cmm": 240, "power_consumption_w": 62, "star_rating": 3
| # | sku | title | fan_type | sweep_size_mm | rpm | air_delivery_cmm |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Home Appliances objects from orientelectric.com. All fields typed and schema-versioned.
"sku": "GEYSER-AURA-25L", "title": "Aura Plus Water Heater 25L", "capacity": "25L", "power_w": 2000, "heating_element": "Copper", "body_material": "ABS Plastic", "price": 6490.0, "stock_status": "In Stock"
| # | sku | title | category | capacity | power_w | heating_element |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Switchgears objects from orientelectric.com. All fields typed and schema-versioned.
"sku": "MCB-SP-10A-C", "series": "Shield", "title": "Single Pole 10A C-Curve MCB", "poles": "1P", "current_rating_a": 10, "breaking_capacity_ka": 10, "curve_type": "C", "price": 145.0
| # | sku | series | title | poles | current_rating_a | breaking_capacity_ka |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dealer Network objects from orientelectric.com. All fields typed and schema-versioned.
"dealer_id": "DLR-BLR-402", "name": "Sri Lakshmi Electricals", "type": "Authorized Retailer", "city": "Bengaluru", "state": "Karnataka", "pincode": "560034", "latitude": 12.9254, "longitude": 77.6212
| # | dealer_id | name | type | address | city | state |
|---|---|---|---|---|---|---|
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Our scraper handles the dynamic rendering of orientelectric.com, extracting deeply nested technical specifications, dealer mapping APIs, and pricing signals without triggering rate limits.
Extract sweep size, RPM, lumen output, wattage, and motor types across all product categories. Normalised into structured columns.
Capture listed price, MRP, and discount percentages. Track pricing changes over time with timestamped run data.
Extract energy efficiency ratings and power consumption metrics critical for compliance and comparative analysis.
Scrape the dealer network API to extract names, addresses, contact details, and precise lat/long coordinates for all authorised sellers.
Track in-stock and out-of-stock indicators across the D2C storefront to gauge inventory depth and supply chain health.
Identify and extract links to user manuals, installation guides, and warranty documents associated with each SKU.
Map related products and recommended accessories to build complete product relationship graphs.
Preserve the exact site hierarchy from top-level categories down to sub-variants for accurate catalogue mapping.
Run continuous pipelines that only output changed records. Minimise downstream processing for price and stock updates.
Brief in. Clean data out.
Specify the categories, dealer regions, or specific SKUs required. We design the target schema.
We configure Playwright crawlers, proxy routing, and API interception for orientelectric.com.
Schema validation, unit testing on edge cases, and data type verification before production launch.
Structured data pushed to your S3 bucket, Webhook, or API endpoint on your defined schedule.
Extracting clean data requires navigating dynamic frontend frameworks and hidden APIs. Here is how our infrastructure handles the load.
The Orient Electric frontend relies heavily on JavaScript for loading product variants and technical specifications. We use full Playwright sessions to execute the necessary scripts and wait for network idle states before parsing the DOM.
Instead of screen-scraping the map interface, our crawlers intercept the underlying XHR requests to the dealer locator API, extracting clean JSON payloads containing exact lat/long coordinates and contact details.
Aggressive crawling triggers firewall blocks. We implement automated request pacing, randomised jitter, and residential proxy rotation to mimic standard user behaviour and maintain continuous access.
Technical specifications often vary in format between fans and lighting categories. Our pipeline includes post-processing layers to clean units (e.g., standardising 'W', 'Watts', 'watt') into normalised numeric fields.
Pricing and availability can depend on the selected delivery pincode. We manage session state and inject target pincodes to extract region-specific data variants.
Rival appliance and lighting brands monitor Orient Electric D2C pricing and discount strategies to adjust their own market positioning.
Large format retailers analyse the complete catalogue to identify gaps in their own inventory and optimise shelf space allocation.
Sustainability analysts aggregate BEE star ratings and power consumption metrics to benchmark industry energy standards.
Market expansion teams extract dealer locations to map distribution density and identify underserved geographic regions.
Construction firms and bulk buyers track switchgear and lighting specifications to build automated procurement databases.
Financial analysts correlate product launch velocity and category depth with public earnings reports to estimate market penetration.
"Orient Electric provides a masterclass in appliance specifications, but extracting that data cleanly requires bypassing dynamic frontends and map APIs."
Building a scraper for consumer electrical brands involves more than just parsing HTML. It requires intercepting XHR requests for dealer networks, normalising inconsistent technical units across hundreds of product categories, and managing session state for pincode-specific pricing. DataFlirt handles the extraction complexity so your team can focus on data modeling.
Everything supported by our orientelectric.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 orchestrates the crawl while Playwright handles the heavy lifting of JavaScript rendering and API interception on dynamic pages.
We route requests through Indian residential IPs to avoid data center blacklists and ensure access to region-specific content.
Airflow manages scheduling and dependencies, executing containerised workloads on AWS ECS with full state tracking in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About orientelectric.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product specifications, pricing, and dealer locations is generally permissible. DataFlirt extracts only public data and does not bypass authentication walls or collect PII. Clients must review their own use cases against applicable regulations.
We intercept the XHR requests made by the frontend map interface to the backend API. This yields clean, structured JSON containing exact addresses, phone numbers, and coordinates without relying on fragile DOM parsing.
Yes. We can configure the pipeline to inject specific target pincodes into the session state, allowing us to extract region-specific stock status and delivery estimates.
For pricing and stock monitoring, we typically run daily or intra-day schedules. Full catalogue technical specification extracts are usually scheduled weekly or monthly.
Yes. We apply post-processing rules to clean units of measurement. For example, '1200 mm', '1200mm', and '1.2m' sweep sizes are normalised into a standard numeric field.
We build managed pipelines for specific scopes. Contact us with your target categories or data points for a technical evaluation and pricing quote.
Yes. We provide sample JSON/CSV extracts of specific categories (e.g., Ceiling Fans or LED Bulbs) during the scoping phase to ensure the schema meets your requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually copying specifications and dealer locations. We build and maintain the extraction pipeline so you get clean, structured data on your schedule.
