We extract furniture listings, pricing signals, branch-level stock, and technical specifications from Poco.de. 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 poco.de. All fields typed and schema-versioned.
"sku": "508923400", "title": "Ecksofa Grau", "category": "Wohnzimmer", "price": 499.99, "discount_pct": 15, "online_available": true, "dimensions": "250x150x80 cm"
| # | sku | title | category | sub_category | brand | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Store Inventory objects from poco.de. All fields typed and schema-versioned.
"sku": "508923400", "store_id": "P045", "store_name": "POCO Berlin-Wedding", "stock_level": 4, "stock_status": "in_stock", "pickup_available": true
| # | sku | store_id | store_name | city | zip_code | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Promotions objects from poco.de. All fields typed and schema-versioned.
"sku": "508923400", "current_price": 499.99, "original_price": 599.99, "discount_amount": 100.0, "promo_badge": "Werbung", "financing_available": true, "monthly_rate": 15.5
| # | sku | current_price | original_price | discount_amount | promo_badge | sale_end_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from poco.de. All fields typed and schema-versioned.
"sku": "508923400", "material_composition": "100% Polyester", "weight_kg": 85.5, "assembly_required": true, "warranty_months": 24, "care_instructions": "Feucht abwischen"
| # | sku | material_composition | weight_kg | assembly_required | care_instructions | warranty_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Delivery & Shipping objects from poco.de. All fields typed and schema-versioned.
"sku": "508923400", "delivery_method": "Spedition", "dispatch_time_days": "10-14", "shipping_fee": 49.0, "bulky_goods_surcharge": true, "carrier": "DHL Freight"
| # | sku | delivery_method | dispatch_time_days | shipping_fee | bulky_goods_surcharge | return_policy |
|---|---|---|---|---|---|---|
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Our Poco.de scraper handles the complete catalogue: furniture configurations, dynamic pricing, store-level inventory checks, and technical specifications: with JavaScript rendering and anti-bot circumvention built in.
Extract SKU, title, descriptions, dimensions, images, and material specifications for all furniture and DIY items.
Scrape stock status and exact item counts across all physical Poco branches in Germany.
Monitor current prices, original prices, discount percentages, and promotional badges timestamped per run.
Extract material composition, care instructions, weight, and mandated energy efficiency ratings for appliances.
Capture shipping fees, dispatch time windows, and bulky goods surcharges per item.
Navigate the taxonomy from main categories down to specific sub-categories and product lines.
Extract monthly installment rates, interest terms, and financing availability flags.
Monitor pickup availability and readiness times per SKU and individual store location.
Run one-off bulk exports or configure continuous pipelines at daily or hourly cadences.
Brief in. Clean data out.
Provide category URLs, search terms, or SKU lists. We map the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for poco.de.
Schema validation, null-rate checks, price-outlier detection, and data typing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Poco.de employs regional blocking and dynamic inventory rendering. Here is how we maintain stable extraction.
Poco.de restricts access based on IP geography to serve correct local pricing and stock. We route all requests through German residential proxy pools to avoid blocks and retrieve accurate regional data.
Store stock levels load via asynchronous API calls rather than static HTML. Our pipeline intercepts these JSON payloads directly, extracting exact stock counts without waiting for full DOM rendering.
Furniture items often have multiple colour and material variations that update dynamically. We use Playwright to trigger variant switches and capture the corresponding SKU and price changes.
Strict request limits per IP trigger CAPTCHAs. We rotate residential proxies, spoof TLS fingerprints, and implement intelligent backoff strategies to maintain high concurrency without bans.
DOM changes in the frontend require constant validation. Our selector strategy uses multiple fallback chains so a layout update does not break your data pipeline overnight.
Furniture retailers track Poco's discount strategies, promotional pricing, and seasonal sales to adjust their own pricing models.
Analyse store-level stock across Germany to map regional demand for specific furniture categories and DIY materials.
Compare your catalogue against Poco's offerings to identify missing product lines and merchandising opportunities.
Track dispatch windows and shipping costs for bulky goods to optimise your own logistics messaging.
Monitor the expansion of DIY and hardware categories within the discount sector to inform investment strategies.
Analyse price fluctuations across raw material-heavy goods like timber, textiles, and metal fixtures.
"Poco.de holds critical pricing and inventory signals for the German discount furniture market: but extracting it requires navigating aggressive regional blocks and dynamic frontend rendering."
Most teams underestimate the complexity of scraping modern retail sites. Reliable Poco.de extraction requires German residential proxies, Playwright for asynchronous inventory calls, and daily selector maintenance. DataFlirt absorbs that infrastructure overhead so your engineers focus on data modelling.
Everything supported by our poco.de 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 retry logic. Playwright handles JavaScript rendering, cookie sessions, and dynamic variant hydration.
We maintain pools of German residential ISP proxies. Rotation happens per-request to prevent rate limiting and ensure accurate local stock visibility.
Pipelines run on AWS Lambda and ECS. 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 poco.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and store inventory information is generally permissible under EU law. We do not extract personal data, circumvent authentication walls, or violate GDPR.
We iterate through branch IDs and intercept the asynchronous API calls that populate the frontend stock indicators, yielding exact item counts per store.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per SKU for current price, original price, and promotional status.
We route requests through German residential proxies to ensure we receive the correct local pricing and avoid regional access blocks.
A full catalogue scrape typically completes in 4-6 hours depending on the required depth of variant mapping and store inventory checks.
Yes. We extract standard shipping fees, dispatch timeframes, and specific bulky goods surcharges per SKU.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price and inventory monitoring across all branches: we scope, build, and operate the pipeline.
