We extract sofa configurations, fabric variants, dimension metrics, finance pricing, and delivery estimates from DFS. 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 Catalogue objects from dfs.co.uk. All fields typed and schema-versioned.
"sku": "SFA-392-BLU", "title": "French Connection Zinc 4 Seater Sofa", "category": "Sofas", "base_price": 999.0, "sale_price": 899.0, "guarantee_years": 15, "dimension_summary": "H88 x W227 x D93 cm"
| # | sku | title | category | sub_category | base_price | sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fabric & Variants objects from dfs.co.uk. All fields typed and schema-versioned.
"sku": "SFA-392-BLU-VEL", "parent_sku": "SFA-392", "fabric_name": "Teal Velvet", "fabric_type": "Velvet", "colour_family": "Blue", "variant_price": 949.0, "in_stock": true, "swatch_available": true
| # | sku | parent_sku | fabric_name | fabric_type | colour_family | variant_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Finance Options objects from dfs.co.uk. All fields typed and schema-versioned.
"sku": "SFA-392-BLU", "cash_price": 899.0, "deposit_amount": 0.0, "monthly_payment": 18.72, "term_months": 48, "apr_pct": 0.0, "total_payable": 899.0, "finance_provider": "Novuna"
| # | sku | cash_price | deposit_amount | monthly_payment | term_months | apr_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dimensions & Specs objects from dfs.co.uk. All fields typed and schema-versioned.
"sku": "SFA-392-BLU", "height_cm": 88.0, "width_cm": 227.0, "depth_cm": 93.0, "seat_height_cm": 45.0, "seat_depth_cm": 60.0, "frame_material": "Hardwood"
| # | sku | height_cm | width_cm | depth_cm | internal_width_cm | seat_height_cm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Stock objects from dfs.co.uk. All fields typed and schema-versioned.
"sku": "SFA-392-BLU", "postcode": "M1 1AA", "delivery_lead_time_weeks": 4, "stock_status": "Made to order", "click_and_collect_eligible": false, "delivery_cost": 79.0, "assembly_included": true
| # | sku | postcode | delivery_lead_time_weeks | stock_status | click_and_collect_eligible | store_availability |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our DFS scraper navigates complex product configurators, extracting every permutation of fabric, colour, and finance option alongside precise dimension metrics.
Capture titles, descriptions, frame guarantees, and care instructions across sofas, beds, dining, and living categories.
Extract every combination of fabric type and colour family, mapping variant-specific pricing and high-resolution imagery.
Record base prices, sale discounts, and exact finance calculator outputs including deposit, term length, and APR.
Extract and normalise height, width, depth, seat height, and internal dimensions into structured numeric fields.
Input target postcodes to scrape dynamic delivery estimates, stock status, and assembly service availability.
Scrape customer ratings, review text, and recommendation percentages paginated across all product reviews.
Map the exact breadcrumb structure from primary category down to specific collections and designer collaborations.
Download URLs for main gallery images, lifestyle shots, and specific fabric swatch thumbnails.
Run continuous pipelines to detect price changes, discontinued fabrics, or shifts in delivery lead times.
Brief in. Clean data out.
Provide target categories, specific collections, or postcodes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for dfs.co.uk.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from modern furniture retailers requires rendering complex 3D configurators and stateful finance calculators. Here is how we build resilient pipelines.
DFS product pages use heavy JavaScript to update pricing, imagery, and stock status when a user selects a different fabric or colour. We use Playwright to programmatically iterate through every swatch, waiting for network idle to capture the correct variant state.
Finance terms update dynamically based on the selected variant price and user-defined deposit. Our crawlers interact with the DOM to extract the precise matrix of term lengths, APRs, and monthly payments for every product configuration.
Delivery estimates require a valid UK postcode. We inject target postcodes into the session state to retrieve accurate, localised delivery weeks and assembly costs, rather than relying on generic national averages.
Dimension data is often presented in varied HTML table structures or raw text blocks. We use XPath fallbacks and regex parsing to consistently extract height, width, and depth into clean, queryable numeric columns.
To prevent rate limiting during high-volume variant iteration, we route requests through UK-based residential proxies, maintaining session continuity for stateful interactions.
Furniture retailers track DFS base prices, promotional discounts, and finance offers to maintain competitive positioning.
Merchandisers monitor the introduction and discontinuation of specific fabric colours and frame styles to identify consumer trends.
Analysts track fluctuations in delivery lead times across different collections to gauge manufacturing backlogs and demand spikes.
Design teams use aggregated dimension data to standardise sizing for compact living versus large-scale modular sofas.
Financial analysts track the prevalence of 4-year interest-free offers and deposit requirements across the catalogue.
Investors correlate review velocity and stock availability indicators to estimate category-level performance.
"DFS holds the UK's most comprehensive dataset on upholstered furniture trends, pricing, and consumer finance preferences."
Extracting this requires more than simple HTTP requests. Fabric configurations, dynamic finance calculators, and postcode-dependent delivery estimates demand full browser rendering and session management. DataFlirt handles this complexity natively, delivering structured catalogue data ready for analysis.
Everything supported by our dfs.co.uk 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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across UK regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About dfs.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from DFS is generally permissible under UK law. DataFlirt targets only public, non-authenticated product, pricing, and variant data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should consult legal counsel for specific use cases.
DFS product pages use JavaScript to load variant data. We use Playwright to programmatically iterate through all available fabric and colour swatches on a product page, capturing the specific price, image, and stock status for each combination.
Yes. We can inject a target UK postcode into the session state during the crawl to extract localised delivery lead times and assembly availability.
We interact with the on-page finance calculator widget, extracting the matrix of available terms, deposit requirements, monthly payments, and APR percentages for the selected product.
Yes. We parse the dimension tables or text blocks on the product page, normalising height, width, depth, and internal measurements into structured numeric fields in centimetres.
Full catalogue refreshes at daily or weekly cadences complete within a 4-8 hour window depending on variant depth. Subsequent runs emit only changed records to optimise downstream processing.
Our packages start with a defined category scope (e.g., all sofas and beds) with weekly delivery. Contact us with your specific requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous monitoring of furniture pricing and lead times — we scope, build, and operate the pipeline. Tell us what you need.
