HBase-compatible, enterprise-grade NoSQL database service with single-digit millisecond latency, limitless scale, and 99.999% availability for large analytical and operational workloads.
New customers get $300 in free credits to spend on Bigtable.
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 scale to hundreds of nodes dynamically supporting peak demand at low latency. Replication also adds high availability and workload isolation for live serving apps.
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.
Creating a Cloud Bigtable instance
Create a Cloud Bigtable instance using command-line tools or the Cloud Console.
Quickstart using the cbt tool
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.
Migrating from HBase to Cloud Bigtable with minimal downtime
Use tooling designed to create Cloud Bigtable tables from HBase tables schemas, import snapshots of the HBase tables, and validate the integrity of migrated data.
Let Cloud Bigtable automatically add or remove nodes when usage changes, significantly lowering the risk of over-provisioning or under-provisioning your resources.
Cloud Bigtable for Cassandra users
Understand the similarities and differences between Cloud Bigtable and Apache Cassandra so you can migrate existing applications or build new ones using Bigtable.
Cloud Bigtable client libraries
Work with Cloud Bigtable using a Google Cloud client library in your preferred programming language.
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.
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.
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.
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.