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  • Deploy CDC and Streaming ETL in Minutes Using Estuary
    Real-time 101 (30 min)
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    Batch for when real-time doesn't fit right-time

    Power reliable analytics and reporting workflows with predictable batch data movement that fits naturally into how your team already works.

    Watch VideoBook A Demo
    Estuary batch data pipeline with scheduled extraction and delivery control

    Production use cases

    Connect once. Deliver anywhere.

    Estuary connects to your existing databases, SaaS tools, and data sources and delivers batch loads to every downstream destination through the same unified pipeline, on whatever schedule your team needs.

    MySQL relational database sourceOracle relational database sourceAmazon DynamoDB sourceMongoDB document database sourcePostgreSQL relational database sourceBatch delivery destinations supported by EstuaryDownstream categories include data warehouses, data lakes, analytics tools, search, and AI systems.Data warehouse destinations for batch loadsDataWarehousesData lake destinations for batch loadsDataLakesAnalytics and BI tools receiving batch dataAnalyticsToolsSearch systems receiving batch dataSearchSystemsAI and machine learning systems receiving batch dataAISystemsBatch data integration diagram: sources connect through Estuary to data warehouses, data lakes, analytics tools, search, and AI systemsOracle, MySQL, DynamoDB, PostgreSQL, and MongoDB feed a unified Estuary batch pipeline that delivers to data warehouses, data lakes, analytics tools, search, and AI systems.Extract data once into the unified Estuary pipelineExtract onceDeliver batch data to every downstream destinationEvery destinationControl delivery timing and schedules per destinationOn your scheduleEstuary product logo

    Why it matters

    Silent failures, schema mismatches, and unclear ownership are symptoms of fragmented batch tooling. Estuary brings all your batch pipelines into one place, with automated schema handling and zero-downtime backfills.

    Before: Pipeline Chaos

    Alerts and failures from fragmented pipelines and batch toolingA collage of alert cards showing pipeline chaos: schema mismatches, connector failures, stale warehouse data, and unclear ownership. Decorative example UI from the batch data integration page.Coworker Coworker Warehouse data is 6 hours stale. Did the batch job fail?Warehouse data is 6 hours stale.Did the batch job fail?Streaming pipeline Streaming pipeline Schema mismatch in Transform layer Schema mismatch in Transform layer CDC Connector failed CDC Connector failed Coworker Coworker Who owns the real-time pipeline? I need someone to restart it.Who owns the real-time pipeline?I need someone to restart it

    After: Centralized Execution

    Pipeline health dashboard with active flows and reliability metricsA pipeline health card showing active flows, real-time streams, batch pipelines, CDC connectors, and last 24 hours metrics including failed jobs, records processed, average latency, and uptime.Pipeline health Pipeline health All systems operational All systems operational Active flows Active flows 47 active flows 47 28 real-time streams 28 Real-time streams Real-time streams 12 batch pipelines 12 Batch pipelines Batch pipelines 7 CDC connectors 7 CDC connectors CDC connectors Last 24 hours Last 24 hours 0 failed jobs0 failed jobs2.4B records processed2.4B records processed89ms average latency89ms avg. latency99.9% uptime99.9% uptime

    Batch pipeline capabilities

    • Execution

      • Declarative scheduling
      • Incremental and full-load support
      • Extract once, deliver to multiple destinations

    • Data Integrity

      • Incremental or full backfills
      • Fully automated schema evolution
      • Historical reprocessing without re-extraction

    • Cost + Observability

      • Predictable costs
      • OpenMetrics API for detailed metrics
      • Fine-grained monitoring and alerts

    Technical highlights

    • High-Throughput Parallel Extraction Engine

      Bulk and incremental loads with adaptive parallelism, rate limiting, and per-connector throughput control.

    • Exactly-Once Batch Delivery

      Idempotent writes and deterministic checkpoints ensure safe re-runs without duplication.

    • Deterministic Backfills at Any Scale

      Reprocess historical data without re-extracting from source systems.

    Uri Vinetz — testimonial author avatar

    “We needed something self-serve, fast, and reliable, and Estuary delivered exactly that. It’s a huge unlock for our operations, reporting, and machine learning.”

    Uri Vinetz, Director of Data, Livble
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    One platform for all data movement

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    Frequently asked questions

      When should I use batch instead of real-time?

      Batch is ideal for scheduled workflows like nightly reporting, large-scale data migrations, and cost-optimized analytics pipelines where instant updates aren’t required.

      Yes. Estuary supports deterministic backfills and replay, allowing you to reprocess historical data without re-extracting from source systems.

      Through exactly-once delivery and checkpointing, ensuring no duplication or data loss even during retries or failures.

      Yes. Estuary is built for “right-time data,” meaning you can run both batch and real-time pipelines in the same platform without duplication or reconfiguration.

      Estuary eliminates fragmented pipelines by unifying batch and real-time processing, reducing operational complexity and improving reliability.