We extract fine jewelry catalogues, dynamic pricing grids, gemstone quality variations, and customer reviews from Angara. 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 Product Listings objects from angara.com. All fields typed and schema-versioned.
"SKU": "SR0145S", "title": "Classic Oval Blue Sapphire Solitaire Ring", "category": "Rings", "sub_category": "Sapphire Rings", "base_price": 859.0, "currency": "USD", "primary_gemstone": "Blue Sapphire", "gemstone_shape": "Oval"
| # | SKU | title | category | sub_category | base_price | currency |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Dynamic Pricing objects from angara.com. All fields typed and schema-versioned.
"SKU": "SR0145S", "configuration_id": "SR0145S-14KY-A-1.5", "metal_type": "Yellow Gold", "metal_purity": "14k", "stone_quality": "Heirloom (AAAA)", "carat_weight": 1.5, "calculated_price": 2450.0, "availability_status": "In Stock"
| # | SKU | configuration_id | metal_type | metal_purity | stone_quality | carat_weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Gemstone Specs objects from angara.com. All fields typed and schema-versioned.
"SKU": "SR0145S", "stone_type": "Blue Sapphire", "creation_method": "Natural", "enhancement": "Heated", "quality_grade": "Heirloom", "dimensions": "8x6 mm", "setting_type": "Prong", "number_of_stones": 1
| # | SKU | stone_type | creation_method | enhancement | quality_grade | dimensions |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from angara.com. All fields typed and schema-versioned.
"review_id": "REV-98234", "SKU": "SR0145S", "rating": 5, "review_date": "2023-11-14", "review_title": "Stunning clarity and colour", "verified_buyer": true, "helpful_votes": 12
| # | review_id | SKU | reviewer_name | rating | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from angara.com. All fields typed and schema-versioned.
"category_name": "Engagement Rings", "search_term": "emerald cut", "position": 4, "SKU": "ER9021E", "starting_price": 1200.0, "rating": 4.8, "review_count": 45
| # | category_name | search_term | position | SKU | title | starting_price |
|---|---|---|---|---|---|---|
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Angara's catalogue relies on dynamic JavaScript widgets to calculate prices based on metal, stone quality, and carat. Our infrastructure evaluates these permutations to deliver flat, queryable pricing grids.
Extract all pricing permutations by programmatically selecting metal, carat, and gemstone quality combinations from the client-side widget.
Map Good, Better, Best, and Heirloom (AAAA) quality grades to their respective price premiums and dimension specifications.
Capture base price shifts across 14k gold, 18k gold, platinum, and silver alloys for every applicable SKU.
Extract high-resolution image URLs, 360-degree view assets, and video links associated with specific metal colour selections.
Scrape paginated customer feedback, including star ratings, verified purchase flags, and detailed review text.
Map the full taxonomy of rings, necklaces, earrings, and bracelets, preserving parent-child category relationships.
Identify products offering GIA, IGI, or internal certificates, capturing the exact certification parameters provided.
Extract estimated shipping dates, made-to-order lead times, and out-of-stock statuses per configuration.
Run pipelines daily or weekly to track retail price adjustments correlated with global precious metal and diamond market indices.
Brief in. Clean data out.
Provide category URLs or specific jewelry types. We design the configuration extraction schema together.
We configure Playwright crawlers to handle Angara's JavaScript pricing widgets and iterate through permutation matrices.
Schema validation, null-rate checks, and permutation completeness verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting jewelry data requires hydrating complex JavaScript pricing widgets and iterating through thousands of metal/stone permutations per product.
Angara calculates variant pricing in the browser. We deploy Playwright instances to interact with dropdowns and capture the resulting DOM updates, ensuring accurate pricing for every configuration.
A single ring might have 4 metal types, 4 stone qualities, and 5 carat weights — yielding 80 distinct SKUs. Our pipeline automatically generates and queries every valid combination to build a complete pricing matrix.
To prevent IP bans while traversing thousands of product permutations, we route requests through residential proxies with realistic browser fingerprinting and request pacing.
Product images change based on the selected metal and gemstone. We map specific high-resolution CDN URLs to their corresponding configuration IDs.
Fine jewelry prices fluctuate with gold and diamond markets. We maintain state across pipeline runs, emitting only the configurations where prices or lead times have changed.
DTC jewelry brands monitor Angara's pricing matrices to benchmark their own markup on specific metal and gemstone combinations.
Merchandisers analyse catalogue breadth across gemstone shapes and quality tiers to identify missing product categories in their own stores.
Analysts track how quickly retail prices adjust to wholesale gold and platinum index changes.
Product teams mine customer feedback to understand preferences regarding setting durability, stone clarity, and sizing accuracy.
Consultancies track shifts in popular gemstone choices (e.g., sapphire vs emerald) based on review velocity and category positioning.
Computer vision teams extract categorised, high-resolution jewelry imagery to train classification and recommendation models.
"Fine jewelry pricing is intrinsically multidimensional. A single Angara ring generates over 40 distinct price points based on metal, carat, and gemstone quality permutations."
Scraping static HTML from Angara yields only base prices. True competitive intelligence requires executing the client-side JavaScript pricing engine, iterating through every configuration dropdown, and normalising the output. DataFlirt manages this execution overhead so you receive clean, flat pricing matrices ready for immediate analysis.
Everything supported by our angara.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 URL discovery and pipeline orchestration, while Playwright manages the complex JavaScript interactions required to reveal dynamic pricing.
Custom middleware designed to map and execute all valid combinations of product attributes, ensuring no price point is missed.
Containerised workloads scheduled via Apache Airflow, scaling dynamically on Kubernetes to handle the heavy compute requirements of headless browser execution.
Data delivered to where your team already works — no new tooling required.
About angara.com scraping, legality, and pipeline operations.
Ask us directly →Our pipeline uses headless browsers (Playwright) to programmatically interact with the configuration dropdowns on the product page. We generate a matrix of all possible options (e.g., 14k Gold + Heirloom Sapphire + 1.5 Carat) and capture the resulting price updated by the site's JavaScript.
Yes. As we iterate through metal types (e.g., switching from Yellow Gold to Rose Gold), we capture the specific CDN URLs for the updated product imagery, allowing you to map exact images to exact SKUs.
Yes. We maintain a historical database of your extractions. By running the pipeline on a scheduled cadence (e.g., weekly), we can deliver diff files that highlight exactly which configurations have increased or decreased in price.
If the certification details are surfaced on the product page or within the technical specifications tab, we extract them. This includes the certifying body and any specific grades mentioned.
Because Angara requires heavy JavaScript execution to evaluate thousands of permutations, a full catalogue run typically takes 12-24 hours depending on the configured concurrency limits and proxy rotation strategy.
Yes. During the scoping phase, we run a sample extraction on a small subset of products (e.g., 10 rings) to demonstrate the flat pricing matrix and ensure the schema meets your analytical requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue extract or continuous price monitoring across thousands of gemstone permutations — we scope, build, and operate the pipeline.
