Estuary

Estuary Flow

Estuary Flow is the only platform purpose-built for real-time ETL and ELT data pipelines. Batch-load for analytics, and stream for ops and AI - all set up in minutes, with millisecond latency.

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  • 30-day free trial
Real-time ETL with Estuary Flow: Seamlessly move data from source to destination for immediate analysis and actionable insights.

How it works

    Key features

    Estuary Flow stands out because it brings together the best of CDC, real-time, and batch with modern data engineering best practices, enabling the best of both worlds, without managing infrastructure.

    Connect apps, analytics, and AI using 100s of streaming CDC, real-time, and batch no-code connectors built by Estuary for speed and scale.

    Perform end-to-end streaming CDC.

    • Stream transaction logs + incremental backfill.
    • Capture change data to a collection.
    • Reuse for transformations or destinations.

    Use Flow Dekaf to connect any Kafka-compatible destination to Flow as if it were a Kafka cluster via the destination's existing Kafka consumer API support.

    Connect apps, analytics, and AI using 100s of streaming CDC, real-time, and batch no-code connectors built by Estuary for speed and scale.

    As you capture data, Flow automatically stores each stream as a reusable collection, like a Kafka topic but with unlimited storage. It is a durable append-only transaction log stored in your own private account so you can set security rules and encryption.

    Flow

    Transform and derive data in real-time (ETL), using SQL or Typescript for operations, or use dbt to transform data (ELT) for analytics.

    Move data from many sources to collections, then to many destinations all at once. Share and reuse data across projects, or replace sources and destinations without impacting others.

    Reuse collections to backfill destinations enabling fast and effective one-to-many distribution, streaming transformations and time travel, at any time.

    Automatically inferred and managed from source to destination using schema evolution.

    • Automated downstream updates.
    • Continuous data validation and testing.

    CLI and API Automation using flowctl.

    Deploy each capture, SQL or TypeScript task, and materialization of a single pipeline in the same or different public or private clouds and regions.

    Create a data pipeline in minutes

    Build new data pipelines that connect many sources to many destinations in minutes.

    Create a data pipeline - Step 1

    1

    Add 100s of sources and destinations using no-code connectors for streaming CDC, real-time, batch, and SaaS. (see connectors).

    Create a data pipeline - Step 2

    2

    Choose any speed for each connection from real-time to hour+ batch; schedule fast updates when you need them to save money.

    Create a data pipeline - Step 3

    3

    Write in-place updates or the full change history into a destination.

    The same data across analytics, Ops, and AI

    Add data from your sources into collections. Then reuse that data for any destinations in real-time or batch.

    Analytics
    Ops
    AI
    Estuary logo
    Flow path with arrow
    Analytics Connectors

    Load into BigQuery, Databricks, Redshift or Snowflake for analytics.

    Coding optional

    Configure or code

    Choose the best combination of no-code configuration and coding to move and transform data.

    • Use 100s of no-code connectors for apps, DBs, DWs, and more.

    • Use the Flow UI to build without coding, or the flowctl CLI for development.

    • Transform using Streaming SQL (ETL) and Typescript (ETL) or dbt (ELT) in your warehouse.

    Coding optional

    Use modern DataOps

    Rely on built-in data pipeline best practices, integrate tooling, and automate DataOps to improve productivity and reduce downtime.

    • Automate DataOps and integrate with other tooling using the flowctl CLI.

    • Use built-in pipeline testing to validate data and pipeline flows automatically.

    • Select advanced schema detection and automate schema evolution.

    Schema evolution options
    Maximize efficiency, reduce expenses

    Increase productivity, lower costs

    • Be 4x more productive and focus more new development, less on troubleshooting.

    • Spend 2-5x less with low, predictable pricing (see pricing.)

    • Minimize source loads and costs by extracting data only once from each source.

    • Lower destination costs by using real-time extraction with batch loading. Then schedule faster updates only when you need them.

    Success story - Connect & Go logo

    Connect&GO

    Connect&GO lowers MySQL to Snowflake latency up to 180x, improves productivity 4x with Estuary.

    Success story - Soli & Company logo

    Soli & Company

    Soli & Company trusts Estuary's approachable pricing and quick setup to deliver change data capture solutions.

    Deliver real-time data at scale

    Estuary Flow delivers reliable, real-time performance in production for over 3,000 active users, including some the most demanding workloads proven to 10x the scale of the alternatives.

    • Stream in real-time at any scale, running over 7GB/sec in production just for 1 customer.

    • Ensure data is never lost with exactly-once transactionally storage and delivery.

    • Use built-in monitoring and alerting, and active-active load balancing and failover.

    Estuary performance metrics
    Estuary logo

    7+GB/sec

    Single dataflow

    5500+

    Active users

    <100ms

    Latency

    Secure your data

    We’ve designed Estuary with air-tight compliance standards to make sure your data and systems are secure - no matter what

    • We never store your data - we just help move it

    • HIPAA, GDPR, CCPA and CPRA compliant

    • SOC 2 Type II certified

    Streaming ETL vs. Batch ELT

    Key feature - Real-time and batch

    Streaming ETL

    With Estuary, you extract data exactly and only once using CDC, real-time, or batch; use ELT and ETL; and deliver to many destinations with one pipeline.

    Key feature - Real-time and batch

    Batch

    SaaS ELT tools are batch only, point-to-point replication. Each destination requires its own pipeline and source extractions, adding loads, costs, and time.

    How Estuary Flow compares

    Feature Comparison

    EstuaryBatch ELT/ETLDIY PythonKAFKA
    Price$$$-$$$$$-$$$$$-$$$$
    Speed<100ms5min+Varies<100ms
    EaseAnalysts can manageAnalysts can manageData EngineerSenior Data Engineer
    Scale
    View Comparisons
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