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FASTEST, MOST RELIABLE CDC AND ETL

Stream data from Stripe Real-time to Google Bigquery

Move data from Stripe Real-time to Google Bigquery in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.

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  • 30-day free trial
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  • 200+Of connectors
  • 5500+Active users
  • <100msEnd-to-end latency
  • 7+GB/secSingle dataflow

How to integrate Stripe Real-time with Google Bigquery in 3 simple steps

1

Connect Stripe Real-time as your data source

Set up a source connector for Stripe Real-time in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.

2

Configure Google Bigquery as your destination connector

Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to Google Bigquery.

3

Deploy and Monitor Your End-to-End Data Pipeline

Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.

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Stripe Real-time connector details

The Stripe Real-time connector continuously streams data from the Stripe API into Estuary collections, allowing you to analyze and act on up-to-date financial and customer information across your business.

  • Comprehensive coverage: Captures a wide range of Stripe resources including charges, invoices, payments, subscriptions, refunds, disputes, and more.
  • Real-time ingestion: Streams events as they occur, giving you an always-current view of transactions and account activity.
  • Connected account support: When capture_connected_accounts is enabled, Estuary captures data from all connected Stripe accounts and includes an account_id field in each document.
  • Simple configuration: Requires only your Stripe API key and an optional start_date to begin replication.
  • Flexible sync modes: Supports incremental and full-refresh bindings, making it suitable for both live and historical data ingestion.
  • Secure by design: Uses your private API credentials with encrypted configuration storage in Estuary.

💡 Tip: Use the _start_date_ parameter to control backfill volume and avoid pulling older data when you only need current records.

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See how Curri uses Stripe Real-time

For more details about the Stripe Real-time connector, check out the documentation page.

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Google Bigquery connector details

Estuary’s Google BigQuery materialization connector writes Estuary collections into tables within a BigQuery dataset for scalable analytics and near real-time reporting. The connector stages data through Google Cloud Storage, then applies standard merges or high-speed delta updates so BigQuery stays current as upstream data changes.

  • Materialize Estuary collections into BigQuery tables within a selected BigQuery dataset.
  • Use a Google Cloud Storage bucket as a temporary staging area for reliable delivery into BigQuery.
  • Create the staging bucket in the same region as the destination BigQuery dataset.
  • Support both standard merge mode and delta update mode for performance and update flexibility.
  • Use a Google Cloud service account with access to the BigQuery dataset, BigQuery jobs, BigQuery read sessions, and the staging GCS bucket.
  • Enable clustering by primary keys where appropriate to improve query performance on materialized tables.
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See how Cosuno uses Google Bigquery

For more details about the Google Bigquery connector, check out the documentation page.

Estuary in action

See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.

Success stories

    Spend 2-5x less

    Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.

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    Stripe Real-time to Google Bigquery pricing estimate

    $1,000 / month
    800 GB of data moved
    2 connector instances

    Estimated monthly cost to move 800 GB from Stripe Real-time to Google Bigquery is approximately $1,000.

    Data moved

    Choose how much data you want to move from Stripe Real-time to Google Bigquery each month.

    GB

    Choose number of sources and destinations.

    US VS THE REST

    Why pay more?

    Move the same data for a fraction of the cost.

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    What customers are saying

    Recart avatar

    Istvan Kovacs

    CTO, Recart

    Estuary became our real-time data backbone without the cost or complexity of traditional solutions. We replaced a fragile, high-maintenance pipeline with a managed system that just works and scales.

    Read the Success Story
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    Maximilian Seifert

    CTO, Cosuno

    Estuary just works. We’ve never had an incident, and it cut our data movement costs in half.

    Read the Success Story
    Flashpack avatar

    Flashpack


    We're a big fan of Estuary's real-time, no code model. It's magic that we're getting real time data without much effort and we don't have to spend time thinking about broken pipelines. We've also experienced fantastic support by Estuary.

    Getting started with Estuary

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      Make sure you read through the documentation, especially the get started section.

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    QUESTIONS? FEEL FREE TO CONTACT US ANY TIME!

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    Frequently Asked Questions

      How is pricing calculated for moving data from Stripe Real-time to Google Bigquery?

      Pricing is based on the volume of data moved and the number of active connectors. Use the pricing estimator above to see an estimated monthly cost for your Stripe Real-time to Google Bigquery pipeline.

      Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.

      Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.

      Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.

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