Auto-scaling scraping infrastructure that runs entirely in the cloud โ no servers to provision, no proxy pools to manage. Data lands directly in your S3 bucket, BigQuery dataset, Snowflake table, or PostgreSQL instance on your schedule.
Cloud-based web scraping is the execution of data extraction workloads entirely on managed cloud infrastructure โ serverless functions, containerised crawlers, and distributed compute clusters โ rather than on-premise hardware or self-managed VMs. The defining characteristic is elasticity: the infrastructure scales up automatically when jobs are large, and scales back to zero when idle, so you only pay for compute you actually use.
Traditional scraping setups require maintaining a fleet of servers, managing proxy pools, handling IP rotation, and babysitting cron jobs. Cloud-native scraping abstracts all of that. DataFlirt deploys your scraping jobs on Lambda functions, Fargate containers, or GKE pods depending on workload type โ with automatic retry, dead-letter queues, and delivery to your preferred cloud storage or database.
For data engineering teams, ML pipelines, and startups building data products, the value is getting web data directly into your existing cloud infrastructure without any ops overhead. No new servers. No new tooling to learn. Your data lands in S3, BigQuery, or Snowflake exactly as if it came from any other data source in your stack.
Comprehensive extraction built for reliability, accuracy, and scale.
Scraping jobs run on Lambda, Cloud Functions, or Azure Functions โ zero idle cost, instant scale-out on demand.
Crawler nodes distributed across global edge regions for geo-targeted scraping and latency optimisation.
Data written directly to S3, GCS, Azure Blob, or SFTP โ bypassing intermediate storage entirely.
Native connectors to BigQuery, Snowflake, Redshift, and Databricks for zero-ETL data delivery.
Real-time visibility into crawl job status, record counts, cost spend, and error rates per pipeline.
Fine-grained access controls, IAM role integration, and audit logging for every pipeline and delivery endpoint.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data โ reliably.
{ "job_id": "scrape_7f3a91bc", "status": "completed", "destination": "s3://my-bucket/ecom/2025-06-10/", "records_written": 284193, "format": "parquet", "partitioned_by": "date", "duration_s": 312, "cost_usd": 1.84, "errors": 12, "retried": 9, "next_run": "2025-06-11T02:00:00Z" }
Built on proven open-source tools and cloud infrastructure โ no vendor lock-in.
Jobs run on Lambda or Cloud Run โ cold start optimised for scraping workloads, with warm pool for latency-sensitive jobs.
Failed extractions automatically retried with exponential backoff. Persistent failures routed to dead-letter queues for inspection.
100K+ residential and datacenter IPs distributed across 150+ countries, managed entirely as cloud infrastructure.
Per-job cost tracking so you know exactly what each scraping pipeline costs โ down to the record level.
Deploy DataFlirt scraping infrastructure inside your own AWS/GCP/Azure VPC for complete data residency control.
Centrally managed output schemas with versioning โ breaking changes never silently corrupt downstream tables.
From solo analysts to enterprise data teams โ here's how organizations use this data.
Modern data stacks live in the cloud. Your scraping infrastructure should too. DataFlirt integrates natively with AWS, GCP, and Azure โ delivering web data directly into the storage and compute layers your team already uses, with the same reliability, observability, and cost controls you expect from first-party cloud services. No ops overhead. No new tooling. Just data where you need it.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
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.
