Cloud Bigtable

A fully managed, scalable NoSQL database service for large analytical and operational workloads.

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    Consistent sub-10ms latency—handle millions of requests per second

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    Ideal for use cases such as personalization, ad tech, fintech, digital media, and IoT

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    Seamlessly scale to match your storage needs; no downtime during reconfiguration

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    Designed with a storage engine for machine learning applications leading to better predictions

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    Easily connect to Google Cloud services such as BigQuery or the Apache ecosystem


Fast and performant

Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics.

Seamless scaling and replication

Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps.

Simple and integrated

Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started.

Key features

Key features

High throughput at low latency

Bigtable is ideal for storing very large amounts of data in a key-value store and supports high read and write throughput at low latency for fast access to large amounts of data. Throughput scales linearly—you can increase QPS (queries per second) by adding Bigtable nodes. Bigtable is built with proven infrastructure that powers Google products used by billions such as Search and Maps.

Cluster resizing without downtime

Scale seamlessly from thousands to millions of reads/writes per second. Bigtable throughput can be dynamically adjusted by adding or removing cluster nodes without restarting, meaning you can increase the size of a Bigtable cluster for a few hours to handle a large load, then reduce the cluster's size again—all without any downtime.

Flexible, automated replication to optimize any workload

Write data once and automatically replicate where needed with eventual consistency—giving you control for high availability and isolation of read and write workloads. No manual steps needed to ensure consistency, repair data, or synchronize writes and deletes. Benefit from a high availability SLA of 99.99% for instances with multi-cluster routing (99.9% for single-cluster instances).


Learn from customers using Cloud Bigtable

What's new



Quickstart using the cbt tool

Learn Cloud Bigtable basics in the quickstart that uses the Cloud Console and the cbt command-line tool.

Codelab: Introduction to Cloud Bigtable

Step through a Cloud Bigtable codelab that teaches you how to avoid common schema design mistakes, import data, and then query and use it.

Best Practice
Migrating Data from HBase to Cloud Bigtable

Work with Cloud Bigtable using a Google Cloud client library in your preferred programming language.

Google Cloud Basics
Cloud Bigtable for Cassandra users

Understand the similarities and differences between Cloud Bigtable and Apache Cassandra to migrate existing applications or build new using Cloud Bigtable.

APIs & Libraries
Cloud Bigtable client libraries

Manage access control for Cloud Bigtable at the project, instance, and table level.

Creating a Cloud Bigtable instance

Learn first-hand how to use the cbt command line to connect to a Cloud Bigtable instance, perform basic admin tasks, and read and write data in a table.

Google Cloud Basics
Go global with Cloud Bigtable

Cloud Bigtable's replication capabilities give you the flexibility to make your data available across a region or worldwide.

Google Cloud Basics
Optimize schema performance with Key Visualizer

Key Visualizer lets you see key access patterns in heatmap format to optimize your Cloud Bigtable schemas for improved performance.

Google Cloud Basics
Protect and control your Google Cloud services and data

VPC Service Controls create a security perimeter around data stored in Bigtable, helping mitigate data exfiltration risks.

Use cases

Use cases

Use case
Financial analysis

Build models based on historical behavior. Continually update fraud patterns and compare with real-time transactions. Store and consolidate market data, trade activity, and other data, such as social and transactional data.

Financial analysis use case diagram
Use case

Ingest and analyze large volumes of time series data from sensors in real time, matching the high speeds of IoT data to track normal and abnormal behavior. Enable customers to build dashboards and drive analytics on their data in real time.

IoT use case diagram
Use case

Integrate large volumes of unrefined data from many sources, typically to drive consistent customer activity across channels. Collect and compare large volumes of behavior data across customers to find common patterns that can drive recommendations and sales.

Ex: A simplified, overall flow of data between an on-premises data warehouse like Teradata and BigQuery..



Cloud Bigtable is a fast, fully managed, massively scalable NoSQL database service. For detailed pricing information, please view the pricing guide.



Cloud Bigtable integrates with the Apache® ecosystem and other Google Cloud products to analyze, process, and store data. For more details, refer to Integrations documentation.