A fully managed, scalable NoSQL database service for large
analytical and operational workloads. New customers get $300 in free credits to spend on Google
Cloud during the first 90 days. All customers get free usage
(up to monthly limits) of select products, including
BigQuery, GKE, and more.
A fully managed, scalable NoSQL database service for large analytical and operational workloads.
New customers get $300 in free credits to spend on Google Cloud during the first 90 days. All customers get free usage (up to monthly limits) of select products, including BigQuery, GKE, and more.
Consistent sub-10ms latency—handle millions of requests per second
Ideal for use cases such as personalization, ad tech, fintech, digital media, and IoT
Seamlessly scale to match your storage needs; no downtime during reconfiguration
Designed with a storage engine for machine learning applications leading to better predictions
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.
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).
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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.
Migrating Data from HBase to Cloud Bigtable
Work with Cloud Bigtable using a Google Cloud client library in your preferred programming language.
Cloud Bigtable client libraries
Manage access control for Cloud Bigtable at the project, instance, and table level.
Table-level IAM management
Learn how to create a Cloud Bigtable instance using the Cloud Console or command-line tools.
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.
Go global with Cloud Bigtable
Cloud Bigtable's replication capabilities give you the flexibility to make your data available across a region or worldwide.
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.
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.
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.