Distributed scraping infrastructure that processes hundreds of millions of pages per run — with 100,000+ residential proxy IPs, auto-scaling compute clusters, intelligent scheduling, and petabyte-capable output storage. For organisations where data volume is the bottleneck.
Large-scale web scraping is the systematic collection of web data at volumes — millions to billions of pages — that require distributed computing infrastructure rather than a single server or standard scraping tool. When the scope of a data collection project exceeds what a single machine or small cluster can accomplish within the required time window, large-scale infrastructure becomes necessary. This includes building AI training corpora, indexing entire e-commerce catalogs across thousands of retailers, monitoring millions of product prices in near real-time, or constructing comprehensive web indices for vertical search engines.
The engineering challenges of large-scale scraping are qualitatively different from small-scale collection. At millions of pages per day, proxy IP health management becomes critical — pools must be large enough that no IP is overused, with health monitoring and rotation strategies tuned per target domain. Crawl scheduling must be intelligent — prioritising freshly updated content, respecting per-domain rate limits without starving the overall pipeline, and handling failures without cascading delays. Storage and processing architecture must handle data rates that overwhelm standard databases, requiring columnar formats, streaming ingestion, and distributed processing.
DataFlirt has built and operates large-scale scraping infrastructure capable of processing hundreds of millions of pages per month. Our architecture uses distributed Python worker fleets orchestrated via Airflow, a residential proxy pool exceeding 100,000 IPs across 150+ countries, S3-compatible distributed storage for raw and processed data, and Spark-based batch processing for large-scale data transformation. This infrastructure is available to clients on a project basis or as a dedicated deployment.
Large-scale scraping projects require careful upfront architecture design. The right approach for crawling 50 million e-commerce product pages differs significantly from collecting 500 million news articles. We invest in architecture scoping before any code is written — designing the crawl strategy, data model, storage architecture, and processing pipeline to match your specific volume, latency, and quality requirements.
Comprehensive extraction built for reliability, accuracy, and scale.
240+ concurrent async worker processes distributed across cloud regions, processing pages in parallel with intelligent work queue management.
Residential, datacenter, and mobile proxy pool spanning 150+ countries — with per-domain rotation strategies and health monitoring.
Priority-based crawl scheduling respects per-domain rate limits, prioritises fresh content, and handles failures without pipeline stalls.
S3-compatible distributed storage handles raw HTML archives and processed output at any scale, with tiered storage for cost optimisation.
Worker fleet scales automatically with workload — spinning up for large batch runs and scaling down for scheduled maintenance windows.
Live dashboards showing pages/second throughput, proxy health, error rates, storage utilisation, and per-domain crawl status.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "status": "success", "run_id": "df_large_0821", "started_at": "2025-03-21T00:00:00Z", "completed_at": "2025-03-21T06:14:00Z", "pages_crawled": 84200000, "records_parsed":621480000, "workers": 240, "proxies_used": 18400, "error_rate": "0.12%", "output_gb": 284 }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
aiohttp and asyncio-based workers handle thousands of concurrent HTTP connections per process — the highest-throughput Python scraping architecture.
Apache Airflow orchestrates crawl jobs, manages dependencies, handles retries, and provides pipeline visibility across the full workflow.
Apache Spark processes raw crawl output at scale — deduplication, content extraction, quality filtering, and schema transformation across billions of records.
Per-domain rate limiters respond dynamically to server signals — backing off on 429s and errors, accelerating on healthy responses.
Delta Lake format provides ACID transactions, time-travel queries, and efficient upserts for large-scale scrape datasets that update continuously.
Worker pods scale horizontally on Kubernetes in response to queue depth — delivering elasticity without manual infrastructure management.
From solo analysts to enterprise data teams — here's how organizations use this data.
Building scraping infrastructure that works reliably at hundreds of millions of pages is a specialist distributed systems engineering challenge. Proxy pools degrade, crawlers stall, storage fills, and processing pipelines bottleneck in ways that do not appear at small scale. DataFlirt has built and operates infrastructure that handles these challenges in production — so your large-scale data program runs reliably without requiring you to build a distributed systems team.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
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For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
