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Cloud Bigtable

A fully managed, scalable NoSQL database service for large analytical and operational workloads with up to 99.999% availability.

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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 99.999% for instances with multi-cluster routing across 3 or more regions (99.9% for single-cluster instances).


Learn from customers using Cloud Bigtable



Google Cloud Basics
Customer-managed encryption keys (CMEK)

CMEK provides the ability to create and manage Bigtable instances using Google Cloud Key Management (KMS) encryption keys to protect their data-at-rest.

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.

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: Large grey rectangle labeled Google Cloud encompases, on the left, stacked boxes, top labeled Batch containing Time Series Files / Cloud Storage, bottom labeled Streaming containing Time Series Streaming / Pub/Sub. Arrows move right to Time Series Processing / Dataflow. Arrows right to 6 interconnected boxes: Storage / BigQuery, Storage/Cloud Bigtable, Storage/Cloud Storage, Machine Learning/AI Platform, Processing/ Dataproc, and Analysis/Datalab.
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: From left to right, green rectangle labeled “Constrained Devices Non-TCP (e.g. BLE)” contains 3 device icons. Arrow flows right to pink rectangle labeled “Standard Devices HTTPs,” with 3 device icons. Arrow right to Google Cloud rectangle with Ingestion, Pipelines, Storage, Analytics, and Application & Presentation rectangles. Ingestion contains icons for Pub/Sub, Cloud Monitoring, Cloud Logging. Pipelines has Dataflow. Storage has Cloud Storage, Databases, Cloud Bigtable. Analytics has Dataflow, BigQuery, Dataproc, and Datalab. Application & Presentation has App Engine, Google Kubernetes, and Compute Engine. Arrows interconnect these 4 rectangles.
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.

3 stacked boxes on left. 1 “Beacons proximity notifications.” 2 “Back Office Business Systems.” 3 “Mobile Devices Push Notifications.” 1 and 2 flow right to Google Cloud square containing boxes. First is labeled Messaging / Pub/Sub / Proximity Streams. Arrow right to box labeled Processing / Dataflow / Stream Processing. Arrow down to box labeled Messaging / Pub/Sub / Queued Notification. Arrow down to box labeled Notifications / App Engine / Push to Devices. Arrow moves left to 3rd box in the stack: Mobile Devices. From Processing box, arrows also point right to box labeled Events / Cloud Bigtable / Proximity Events and to box labeled Analytics / BigQuery / Data Warehouse



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.