Stream into Google Bigtable with your free account

Deliver data from databases, SaaS apps, APIs, files, and streaming systems into Google Bigtable using Estuary's pre-built connectors. Run pipelines from real-time streaming to scheduled batch based on connector capabilities.

<100ms to batch
200+ connectors
Managed data pipelines

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01. Select a source02. Transform in-flight03. Deliver to Google Bigtable

Google Bigtable connector details

The Google Cloud Bigtable materialization connector in Estuary delivers data from Estuary collections into tables in a Google Cloud Bigtable instance. Each materialization binding writes to a Bigtable table, with row keys derived from the source collection’s primary key.

Materializes Estuary collections directly into Google Cloud Bigtable tables
One Bigtable table per materialization binding
Row keys derived from the Estuary collection key, including support for composite keys
Automatic creation of destination tables and the **f** column family
Selected fields stored as Bigtable columns, with the full source document also stored as JSON
Soft deletes by default, represented through _meta/op
Optional hard deletes to remove rows from Bigtable when records are deleted upstream
Google Cloud service account authentication using JSON credentials
Google Cloud IAM / Workload Identity Federation authentication
Custom Bigtable endpoint support for Bigtable-compatible APIs

Composite collection keys are encoded so their lexicographic ordering is preserved, which makes prefix-based range scans efficient in Bigtable. The connector stores projected fields individually and also stores the full root document in the flow_document column by default.

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

How to connect your data source to Google Bigtable in 3 easy steps

1
Connect your data source
Select from more than 100 supported databases and SaaS platforms including PostgreSQL, MySQL, SQL Server, MongoDB, and Kafka.
2
Prepare and transform your data
Apply transformations and schema mapping as data moves whether you are streaming in real time or loading in batches.
3
Sync to Google Bigtable
Continuously or periodically deliver data into your destination with support for change data capture and reliable delivery for accurate insights.

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HIGH THROUGHPUT

Distributed, event-driven architecture scales for demanding data workloads.

DURABLE COLLECTIONS

Store data as it moves so you can transform, replay, and deliver it downstream.

FLEXIBLE LATENCY

From <100ms CDC and real-time streaming to scheduled batch, depending on connector capabilities.

From <100ms to batch

Estuary supports CDC, real-time streaming, incremental syncs, and scheduled batch across its connector ecosystem. Bring data from databases, SaaS apps, APIs, files, and event streams into Google Bigtable, with capture and delivery cadence based on connector capabilities and pipeline configuration.

Connect data from operational databases, SaaS applications, APIs, files, and streaming systems to Google Bigtable through Estuary.
Transform and route data through Estuary collections before delivering it to Google Bigtable.

See what you can connect to Google Bigtable:

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Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.

Feature Comparison

	Estuary	Batch ELT/ETL	DIY Python	Kafka
Price	$	$$-$$$$	$-$$$$	$-$$$$
Latency	<100ms to scheduled	5min+	Varies	<100ms
Ease	Analysts can manage	Analysts can manage	Data Engineer	Senior Data Engineer
Scale
Maintenance Effort	Low	Medium	High	High