We extract Cubase editions, VST instruments, expansion packs, and hardware specifications from Steinberg. Delivered as clean JSON, CSV, or Parquet to S3 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 Software Products objects from steinberg.net. All fields typed and schema-versioned.
"product_id": "cubase-pro-13", "title": "Cubase Pro 13", "category": "DAW", "edition": "Pro", "price": 579.0, "currency": "EUR", "included_plugins": 87, "key_features": "['VocalChain plugin', 'Chord Pads', 'Iconica Sketch']"
| # | product_id | title | category | edition | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for VST Instruments objects from steinberg.net. All fields typed and schema-versioned.
"plugin_id": "halion-7", "plugin_name": "HALion 7", "developer": "Steinberg", "category": "Sampler", "price": 349.0, "presets_count": 3700, "disk_space_gb": 37.0, "demo_available": true
| # | plugin_id | plugin_name | developer | format | category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Hardware Interfaces objects from steinberg.net. All fields typed and schema-versioned.
"model_id": "ur22c", "model_name": "UR22C", "analog_inputs": 2, "analog_outputs": 2, "connectivity": "USB 3.0", "phantom_power": true, "midi_io": true, "price": 169.0
| # | model_id | model_name | analog_inputs | analog_outputs | connectivity | phantom_power |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for System Requirements objects from steinberg.net. All fields typed and schema-versioned.
"product_id": "dorico-5", "os_mac": "macOS Monterey, macOS Ventura", "os_windows": "64-bit Windows 10, Windows 11", "ram_min_gb": 8, "ram_recommended_gb": 16, "disk_space_gb": 12.0, "auth_method": "Steinberg Licensing", "internet_required": true
| # | product_id | os_mac | os_windows | ram_min_gb | ram_recommended_gb | cpu_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Updates & Support objects from steinberg.net. All fields typed and schema-versioned.
"article_id": "cubase-13-0-20", "product": "Cubase", "version": "13.0.20", "release_date": "2024-01-24", "download_size_mb": 450.5, "os_compatibility": "['macOS', 'Windows']", "category": "Maintenance Update"
| # | article_id | product | version | release_date | release_notes | download_size_mb |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Steinberg pipeline processes complex software editions, regional pricing variations, and deep technical specifications across DAWs, VSTs, and hardware.
Extract and map feature matrices across Cubase Pro, Artist, and Elements to track exact functionality differences.
Capture EUR, USD, GBP, and JPY pricing by routing requests through regional proxy nodes.
Normalise unstructured OS, RAM, CPU, and disk space requirements into queryable database columns.
Extract I/O counts, preamp types, DSP capabilities, and bundled software inclusions for audio interfaces.
Catalogue loop sets, presets, and instruments including compatibility constraints with host DAWs.
Monitor version numbers, release dates, and patch notes from the Steinberg support portal.
Extract specific EDU pricing tiers and eligibility requirements for academic institutions.
Map complex upgrade and crossgrade pricing paths based on existing software ownership.
Extract user issues, feature requests, and official responses from the Steinberg community forums.
Extract localised product descriptions and specifications across DE, EN, FR, ES, and JP store views.
Brief in. Clean data out.
Provide target categories, product lines, or forum sections. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for steinberg.net.
Schema validation, null-rate checks, and normalisation of system requirements before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting nested software features and dynamic pricing requires precise execution. Here is how we maintain pipeline stability.
Steinberg injects pricing data dynamically based on geographic IP and session cookies. We use Playwright and regional residential proxies to force specific store views, ensuring accurate currency extraction.
Comparing Cubase Pro to Elements involves parsing massive HTML tables with checkmarks and tooltips. We map these DOM structures into boolean arrays representing exact feature availability per edition.
System requirements are often written as free-text paragraphs. We use regex and NLP to extract specific RAM values, OS versions, and disk space requirements into strict integer and array fields.
For product catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs when a new version is released or a price drops, reducing your downstream processing load.
Extracting historical release notes or forum threads requires navigating deeply paginated structures. Our crawlers manage state and deduplicate records to ensure complete corpus extraction without infinite loops.
Audio software developers monitor crossgrade paths and promotional pricing to optimise their own DAW and VST pricing strategies.
VST directories and marketplaces aggregate specifications, pricing, and compatibility data to build comprehensive search engines.
PC building and compatibility tools ingest OS and hardware requirements to advise users on optimal studio computer builds.
Reviewers and retailers extract interface specifications to build automated comparison matrices against Focusrite or Universal Audio.
Academic institutions track EDU pricing tiers and site license costs to forecast annual software procurement budgets.
Product managers conduct feature gap analysis by parsing edition comparison tables across major DAW platforms.
"Steinberg's catalogue dictates professional audio standards. Extracting its matrix of editions, upgrades, and specifications requires deep DOM parsing."
Audio software ecosystems are notoriously complex. A single DAW has multiple editions, crossgrade paths, educational discounts, and varying system requirements. DataFlirt parses these multidimensional tables into flat, queryable records so your product team can analyse the audio market directly without building custom parsers for every store update.
Everything supported by our steinberg.net 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 pricing hydration.
We maintain pools of residential ISP proxies across EU, US, and JP regions to force accurate regional pricing views.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About steinberg.net scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from steinberg.net is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and specification data. We do not extract personal data or circumvent MySteinberg authentication walls.
We use geo-targeted residential proxies to simulate traffic from specific countries (e.g., Germany for EUR, USA for USD, Japan for JPY). This forces the Steinberg store to render the correct regional pricing and currency.
Yes. We parse the complex HTML tables comparing Pro, Artist, and Elements editions, mapping checkmarks and text values into boolean arrays representing exact feature availability.
Yes. We scrape the Steinberg support portal to track version histories, release dates, and detailed patch notes for DAWs and plugins.
We use regex and NLP to normalise unstructured text paragraphs into strict database columns for OS versions, minimum RAM, recommended RAM, and disk space.
Yes. We can extract thread titles, post content, user names, timestamps, and pagination structures to build a corpus of user issues or feature requests.
Data is delivered in JSON, CSV, or Parquet formats. We can push directly to AWS S3, Google Cloud Storage, Snowflake, or trigger webhooks for real-time ingestion.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete VST catalogue or continuous DAW pricing intelligence — we scope, build, and operate the pipeline. Tell us what you need.
