Bigtable release notes

In the Google Cloud console, you can import data into Bigtable using the Dataflow job builder, a web interface for building and running Dataflow pipelines. This integration provides a pre-populated template to import data from Pub/Sub to Bigtable. You can also create a custom job to import data from other sources. For more information, see Import and export data.

You can use Cloud KMS Autokey to automate the creation and use of customer-managed encryption keys (CMEK) in Bigtable clusters. This feature is generally available (GA).

Data lineage for Dataflow jobs is generally available (GA) in the Bigtable Beam connector (BigtableIO) and the Bigtable HBase Beam connector (CloudBigtableIO). For more information, see Tracking lineage.

Bigtable is available in the europe-north2 (Stockholm) region. For more information, see Bigtable locations.

Bigtable Data Boost, a serverless compute service designed for high-throughput read jobs and queries, is generally available (GA).

Automated backup for Bigtable is generally available (GA). For more information, see the Backups overview.

Tags data for Bigtable instances is now included in Cloud Billing data, letting you use tagged Bigtable instances to gain visibility into your resource usage and spending. For more information, see Understand standard and detailed usage cost data.

You can now enable 2x node scaling when you create a new Bigtable cluster. This cluster configuration lets Bigtable treat two standard nodes as a larger, single compute node, and the cluster is always scaled in increments of two nodes. This feature is generally available (GA).

The Preview of Bigtable automated backup has been expanded to let you configure the backup retention period in automated backup policies, and the default is now seven days. For more information, see Update an automated backup policy.

You can use Organization Policy Service custom constraints to manage specific operations on Bigtable resources. For more information, see Use custom organization policies. This feature is generally available (GA).

Bigtable is now supported by Database Center, which is in Preview. Database Center is an AI-assisted dashboard that gives you one centralized view across your entire database fleet. With this release, Database Center displays health issues for Bigtable availability and data protection. For more information, see Database health issues.

You can now enable row-affinity routing to let Bigtable automatically ensure that single-row requests for a given row are routed to the same cluster. This feature is generally available (GA).

You can now use the Google Cloud console to create and manage authorized views of your Bigtable tables.

You can now select a row in a Bigtable Studio query results table to view formatted row data. For more information, see Query your data with SQL in the query editor.

To create a Bigtable instance, a user or account must be a principal in a role with the permission bigtable.clusters.create. For more information, see Bigtable access control with IAM.

You can now create a Data Boost app profile and view Data Boost metrics in the Google Cloud console. Data Boost for Bigtable is in Preview. For more information, see Create and configure app profiles.

Bigtable is now available in the northamerica-south1 (Mexico) region. For more information, see Bigtable locations.

Hot backups, optimized backups to restore your data to production performance availability more efficiently, are now generally available (GA). For more information, see Backups overview.

You can perform similarity vector search in Bigtable by finding the K-nearest neighbors. This feature is available as part of the GoogleSQL for Bigtable Preview.

The Preview of Data Boost for Bigtable has been expanded to let you use Data Boost compute resources when you read Bigtable data using a Spark application. For more information, see Use the Bigtable Spark connector.

You can use EXPORT DATA statements to directly export BigQuery data to Bigtable (reverse ETL). This feature is generally available (GA).

You can now enable client-side metrics with the Bigtable client library for Go. Used in conjunction with server-side monitoring metrics, client-side metrics can provide a complete, actionable view of Bigtable performance. For more information, see Set up client-side metrics.

Bigtable supports querying in SQL. This feature is available in Preview. For more information, see Introduction to SQL for Bigtable.

The Bigtable Studio query editor is available in Preview. For more information, see Manage your data using Bigtable Studio.

Bigtable aggregates let you build distributed counters and aggregate your Bigtable data at write time using min, max, sum, or HLL. This feature is generally available (GA). For more information, see Aggregate values at write time.

The Preview of automated backup has been expanded to let you enable and disable automated backup in the Google Cloud console. For more information, see the automated backup documentation.

The Python client library for Bigtable now offers an asynchronous API for use with asynchronous applications. The async API is generally available (GA). To get started, see the Python hello world.