We extract product listings, engineering specifications, CAD model metadata, spare parts hierarchies, and pricing from Festo. 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 Metadata objects from festo.com. All fields typed and schema-versioned.
"part_number": "156636", "order_code": "ADVU-25-15-P-A", "title": "Compact cylinder", "category": "Pneumatic actuators", "sub_category": "Compact, short and flat cylinders", "product_family": "ADVU", "status": "Active"
| # | part_number | order_code | title | category | sub_category | product_family |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technical Specs objects from festo.com. All fields typed and schema-versioned.
"part_number": "156636", "stroke_length_mm": 15, "piston_diameter_mm": 25, "operating_pressure_bar": "1 to 10", "ambient_temp_c": "-20 to 80", "weight_g": 180, "cushioning": "Elastic cushioning rings/plates at both ends"
| # | part_number | stroke_length_mm | piston_diameter_mm | operating_pressure_bar | ambient_temp_c | materials |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for CAD & Documentation objects from festo.com. All fields typed and schema-versioned.
"part_number": "156636", "cad_2d_url": "https://festo.com/cad/2d/156636.dxf", "cad_3d_url": "https://festo.com/cad/3d/156636.step", "datasheet_pdf": "https://festo.com/docs/156636_en.pdf", "operating_instructions_pdf": "https://festo.com/docs/op_156636.pdf", "eplan_macro": "https://festo.com/edata/156636.edz"
| # | part_number | cad_2d_url | cad_3d_url | datasheet_pdf | operating_instructions_pdf | certification_docs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Spare Parts objects from festo.com. All fields typed and schema-versioned.
"parent_part_number": "156636", "spare_part_number": "345678", "spare_part_desc": "Seal kit", "quantity_required": 1, "position_id": "99", "assembly_level": 1, "price": 24.5
| # | parent_part_number | spare_part_number | spare_part_desc | quantity_required | position_id | assembly_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Stock objects from festo.com. All fields typed and schema-versioned.
"part_number": "156636", "list_price": 84.2, "currency": "EUR", "stock_status": "In Stock", "lead_time_days": 2, "min_order_qty": 1, "packaging_unit": "Piece", "region": "DE"
| # | part_number | list_price | currency | stock_status | lead_time_days | min_order_qty |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Festo scraper navigates complex product hierarchies, dynamic configurators, and technical tables to deliver structured engineering data ready for your PIM or ERP.
Extract data across pneumatics, electrical drives, sensors, and process automation components.
Stroke lengths, pressures, temperatures, and materials extracted into structured key-value pairs.
Map 2D/3D model URLs, PDF datasheets, and operating instructions directly to the parent SKU.
Extract parent-child relationships for assemblies, including position IDs and required quantities.
Decode Festo modular type codes into discrete attributes for cross-referencing.
Monitor regional stock levels, lead times, and list prices across different country storefronts.
Extract alternative products, required accessories, and compatible mounting hardware.
Crawl deep, multi-level category trees without missing hidden SKUs or nested families.
Only emit updates when specifications, prices, or lifecycle statuses change.
Brief in. Clean data out.
Provide part numbers, product families, or category URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for festo.com.
Schema validation, null-rate checks, and technical specification formatting before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Industrial catalogues use complex DOM structures and dynamic tables. Here is how we normalise the chaos.
Industrial sites deploy strict rate limits and bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access.
Festo product configurators and dynamic specification tables rely on JavaScript. We run full Playwright browser sessions to trigger lazy-loads and hydrate data tables.
Technical tables vary wildly between pneumatic cylinders and electrical sensors. We use dynamic key-value mapping to normalise these into a consistent schema across product families.
CAD files and datasheets are often nested in separate tabs or require interaction to reveal. Our pipeline systematically clicks through asset tabs to capture all relevant URLs.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops to ensure data consistency.
Optimise spare parts purchasing by mapping component hierarchies and tracking availability.
Map equivalent pneumatic cylinders and actuators by matching technical specifications.
Ingest CAD metadata and technical parameters to build accurate simulation models.
Monitor list prices and lead times across regions to optimise distributor margins.
Populate internal product catalogues with accurate Festo specifications and images.
Track component lifecycle statuses and lead times to prevent production bottlenecks.
"Festo holds the blueprint for modern automation, but extracting that engineering data requires a pipeline built for complex industrial taxonomies."
Most teams struggle with industrial catalogues because the data is buried in dynamic configurators and inconsistent technical tables. DataFlirt absorbs that complexity, mapping deeply nested specifications and CAD links into a clean, queryable format.
Everything supported by our festo.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 crawl orchestration and deduplication. Playwright handles JavaScript rendering for dynamic product configurators.
We maintain pools of residential ISP proxies to bypass industrial WAFs. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About festo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue data is generally permissible. DataFlirt targets only public, non-authenticated product specifications, CAD metadata, and list pricing. We do not extract personal data or circumvent authentication walls.
We use full Playwright browser sessions to execute JavaScript, interact with configurator UI elements, and capture the resulting modular order codes and specifications.
We extract the direct URLs to 2D/3D CAD models (STEP, DXF) and PDF datasheets, mapping them to the parent SKU. We do not host the files, but provide the links for your systems to download.
Our pipeline uses dynamic key-value mapping to parse inconsistent HTML tables. We standardise units (e.g., mm, bar) and field names across different product families for clean downstream ingestion.
Yes. We extract the full spare parts hierarchy, including parent SKU, child part numbers, required quantities, and position IDs.
Yes. We can target specific regional subdomains (e.g., festo.de, festo.us) to capture localised list pricing, currency, and availability.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue extraction or a continuous spare parts monitor, we scope, build, and operate the pipeline. Tell us what you need.
