Lakebase release notes

LTAP Direct Writes is now generally available. It speeds up how synced tables load into Lakebase by writing the initial load and full refreshes directly to the storage layer instead of through your compute endpoint, so large loads finish faster without competing with your queries. You opt in per synced table by selecting LTAP Direct Writes under Synchronization settings when you create the synced table. It's supported on Lakebase projects running Postgres 16, 17, or 18. See Accelerated sync with LTAP Direct Writes.

The following supported Postgres extensions are now available at newer versions. Existing installations keep their current version until you update them with ALTER EXTENSION.

• postgis, postgis_raster, postgis_sfcgal, postgis_tiger_geocoder, postgis_topology, address_standardizer, and address_standardizer_data_us: updated to 3.3.10 on Postgres 16, 3.5.7 on Postgres 17, and 3.6.4 on Postgres 18.
• hll: updated to 2.21 on Postgres 16, 17, and 18.

See Update an extension version.

The new lakebase_tokenizer extension lets you control how Lakebase Search turns text into search terms, so keyword search matches the way your data and users actually read. Native PostgreSQL full-text search gives you little control over this step. lakebase_tokenizer adds a configurable whole-word tokenizer through PostgreSQL's standard text-search dictionary interface, with the controls that matter for relevance:

• Normalization: lowercase text, apply Unicode normalization for byte-stable matching, and strip accents so café matches cafe.
• Stopwords and synonyms: manage your own stopword and synonym lists in SQL, so terms like usa and united_states match the same rows.
• Stemming and possessives: reduce words to their root with a native English stemmer and strip trailing 's, so running matches run and John's matches john.

It produces standard tsvector values, so a tokenizer you configure works with to_tsvector, GIN (generalized inverted index) indexes, and the lakebase_text lakebase_bm25 index for BM25 (best match 25) ranking. Install it after enabling Lakebase Search in your project settings. See lakebase_tokenizer.

Lakebase captures its Postgres observability telemetry into the system.lakebase system tables, with no configuration to create and no destination to choose. Query plans, session history, wait events, schema-change history, database and compute health, and Postgres logs land there, and records are retained for 7 days. Account admins and metastore admins can read the tables and manage access to them, and a user with access queries the telemetry the way they would any Unity Catalog table. See Lakebase telemetry in system tables.

You can now manage Lakebase backup schedules programmatically. The backup schedule API lets you read and set a root branch's automated snapshot cadences (daily, weekly, or monthly) and how long each cadence's snapshots are retained. This API is in Beta and available to all Lakebase workspaces. See Create a backup schedule.

Lakebase Search is now generally available. It brings scalable vector and keyword search to Lakebase projects through two Postgres extensions, so you can run semantic and exact-term search in a single database without a separate search stack:

• lakebase_vector adds the lakebase_ann index type for approximate nearest-neighbor vector search. It is drop-in compatible with pgvector, using the same types, operators, and query syntax, and scales to over 1 billion vectors on a single index with builds up to 50-100x faster than HNSW.
• lakebase_text adds the lakebase_bm25 index type for BM25 full-text search. It works with standard tsvector types and operators, adding BM25 ranking and top-K pushdown that PostgreSQL's native GIN index does not provide.

Indexes are storage-backed, so they are available immediately after scale-to-zero without warmup. Combine both extensions for hybrid search that returns semantic and exact-term matches in one ranked list. To get started, enable Lakebase Search in your project settings, then install the extensions.

See Lakebase Search, lakebase_vector, and lakebase_text.

You can now manage Lakebase snapshots programmatically. The snapshots API lets you create, get, list, and delete point-in-time snapshots of a project branch, and restore a snapshot by creating a new branch from it. This API is in Beta and available to all Lakebase workspaces. See Create a snapshot.

Lakebase is now PCI-DSS and HITRUST compliant in all AWS regions where it is available, for workspaces with the compliance security profile enabled. Previously, this support was limited to the us-east-1 region on AWS. See Data protection.

LTAP Direct Writes speeds up how synced tables load into Lakebase. When you create a synced table, Lakebase loads a full copy of your Unity Catalog data into your Postgres branch. LTAP Direct Writes accelerates that initial load and full refreshes by writing directly to the storage layer instead of through your compute endpoint, so large loads finish faster without competing with your queries. It requires a project running Postgres 17, and you opt in when you create the synced table. See Serve lakehouse data with synced tables.

Lakebase updated its supported Postgres versions to 16.15, 17.11, and 18.6. These minor releases bring you the latest upstream Postgres fixes and security patches. When a new minor version is available, Lakebase applies it the next time your compute restarts. See Manage updates.

A new Genie entry in the Lakebase project sidebar opens Genie Code, the agentic Databricks assistant, scoped to your Lakebase project. Ask it in natural language to run multi-step data and AI tasks: explore your databases and tables, investigate and debug issues, and create and populate tables. It's the same Genie Code you use elsewhere in Databricks, here working on your Lakebase project, and it's now available for all Lakebase projects. See Genie Code.

Programmatic management of Lakebase Postgres is now generally available through the REST API, Databricks CLI, and Databricks SDKs (Python, Java, Go). These interfaces support operations for projects, branches, endpoints, databases, roles, credentials, synced tables, and catalogs. See Lakebase API guide.

Lakebase is now PCI-DSS and HITRUST compliant for workspaces with the compliance security profile enabled. On AWS, this support is limited to the us-east-1 region. On Azure, all regions where Lakebase is available are supported. See Data protection.

Lakebase is now enabled by default in workspaces with the compliance security profile, including workspaces that use the HIPAA, C5, or TISAX standard and workspaces that enable the profile without a compliance standard. You no longer need to enable Lakebase in these workspaces. See Data protection.

Lakebase adds AI-assisted troubleshooting: database issues and early warnings are surfaced to you with Insights, and you can then investigate and fix them conversationally with Genie. Both draw on database telemetry captured to Unity Catalog. See Observability.

Lakebase now provides a Service Level Agreement (SLA), with Monthly Uptime Percentage commitments for endpoints in zone-redundant high availability mode and for single-compute endpoints. For details, see Lakebase SLA.

Lakebase is now SOC 2 Type 2 compliant. For our SOC reports, please ask your Databricks account team or see Compliance.

To deliver Postgres version updates, security patches, and new features to your projects sooner, Lakebase has shortened the planned update notification window. Planned updates are now posted at least 3 days in advance, rather than 7. You can check your project's settings for upcoming updates.

See Manage updates.

Lakebase is now available in ap-northeast-1 (Asia Pacific - Tokyo). For all supported regions, see Region availability. To get started, see Get a Postgres database.

Lakebase now supports Postgres 18. Postgres 17 remains the default version. To use Postgres 18, select it when creating a new project. For a full list of supported versions, see Postgres compatibility.

Lakebase Search brings scalable vector and keyword search to Lakebase projects through two new Postgres extensions, so you can run semantic and exact-term search in a single database without a separate search stack:

• lakebase_vector adds the lakebase_ann index type for approximate nearest-neighbor vector search. It is drop-in compatible with pgvector, using the same types, operators, and query syntax, and scales to over 1 billion vectors on a single index with builds up to 50-100x faster than HNSW.
• lakebase_text adds the lakebase_bm25 index type for BM25 full-text search. It works with standard tsvector types and operators, adding BM25 ranking and top-K pushdown that PostgreSQL's native GIN index does not provide.

Indexes are storage-backed, so they are available immediately after scale-to-zero without warmup. Combine both extensions for hybrid search that returns semantic and exact-term matches in one ranked list. To get started, enable Lakebase Search in your project settings, then install the extensions. See Lakebase Search, or read the announcement blog post.

Starting June 1, 2026, snapshot storage is billable for Lakebase. Snapshot storage size is shown on the Backup & Restore page. For pricing details, see the Lakebase pricing page. To learn about managing snapshots, see Snapshots.

New Lakebase projects now have native Postgres password authentication disabled by default. Existing projects are unaffected. To allow native Postgres roles to connect with static passwords, enable password connections in project settings. See Manage password connections.

Customer-managed keys (CMK) for Lakebase are now available in all Lakebase regions on AWS and Azure. CMK encrypts Lakebase project data at rest using a key your organization owns in its cloud KMS. CMK is available for new Lakebase projects. See Customer-managed keys for Lakebase.

Lakebase now supports compute sizes up to 64 CU (128 GB RAM), up from the previous maximum of 32 CU (64 GB RAM). Larger workloads can now use dynamic scaling. See Autoscaling.

When you delete a Lakebase project, it now enters a soft-deleted state for 7 days before permanent deletion. You can recover the project during that window, restoring all branches, databases, endpoints, and project settings. See Recover a deleted project.

New Lakebase instances now scale to zero by default after 24 hours of inactivity, reducing compute costs for idle projects. To learn more about scale to zero, see Scale to zero. To configure scale to zero, see Configure scale to zero. To change the scale to zero default for new Lakebase projects, see Compute defaults. This change does not affect existing projects.

You can now store every insert, update, and delete on your Lakebase Postgres tables as Unity Catalog managed Delta tables. Lakebase Change Data Feed (CDF) captures row-level changes from the write-ahead log for downstream pipelines, models, and audit, with no external CDC stack to run. See Lakebase Change Data Feed.

Databricks is upgrading all Lakebase Provisioned instances to the Lakebase Autoscaling platform. Upgrades begin in June 2026 for customers who have requested them, with remaining instance upgrades continuing in the following weeks. Workspace admins will receive an email with upgrade dates before the upgrade begins.

The upgrade is automatic. Connections restart briefly during the transition, and existing connection strings, API calls, Declarative Automation Bundles, and Terraform configurations continue to work without modification.

After the upgrade, the following changes apply:

• Your instances will support autoscaling features and can be managed through both the new Autoscaling UI and the familiar Provisioned UI, which remains available until September 1, 2026.

• Each instance receives a new regional connection string that provides optimized ingress:

  • Existing connection strings: Provisioned connection strings (without a region) continue to work over the existing inbound Private Link and do not require Service Direct Private Link.
  • New regional connection string: If you use Private Link and connect to Lakebase from outside the Databricks workspace, you must configure inbound Private Link for performance-intensive services to use the new regional connection string.

• To use new autoscaling features such as scale-to-zero in your Declarative Automation Bundles and Terraform configurations, update them to use autoscaling semantics.

• Lakebase GA pricing applies. With elastic compute replacing fixed-size instances, most customers will see a reduction in compute costs.

• The Forward ETL and REST API Private Preview features on Lakebase Provisioned are disabled after the upgrade. Their replacements, Lakebase Change Data Feed and the Data API, are available on the Autoscaling platform.

Lakebase Autoscaling adds autoscaling and scale-to-zero, point-in-time restore and snapshots, maintenance window scheduling, database branching, and other enhancements. For details about what to expect, what changes, and what actions to take, see Upgrade to Autoscaling.

To request an expedited upgrade or if you have questions, contact your account team or Databricks Support.

You can now export Lakebase Postgres metrics and logs to any OTLP-compatible observability backend, including Grafana Cloud, New Relic, and Datadog. Configure the endpoint, authentication, and resource attributes from the Lakebase App. This feature is in Beta.

See OpenTelemetry export.

You can now manage encrypted Lakebase project data using a customer-managed key (CMK) from your own cloud KMS. This gives your organization full control over encryption and helps meet regulatory and compliance requirements. See Customer-managed keys for Lakebase.

You can now add Lakebase databases as resources for your Databricks app. See Add a Lakebase resource to a Databricks app.

Lakebase now includes the following features:

• OAuth roles: Create OAuth roles using the Lakebase UI or REST API, in addition to SQL. See Create Postgres roles.
• Budget policies and tags: Associate a serverless budget policy and add custom tags to your project for cost attribution and tracking. See Manage projects.
• Lakehouse sync (Beta): Continuously replicate Lakebase Postgres tables to Unity Catalog Delta tables using CDC. See Lakebase Change Data Feed.

New Lakebase instances are now created with autoscaling compute, scale-to-zero, branching, and instant restore by default. This applies when you create instances using the UI, Database instance API, Terraform, or SDKs. See Autoscaling.

Lakebase is now generally available for workspaces with the compliance security profile when the compliance standard is set to HIPAA, C5, TISAX, or None.

See Lakebase compliance.

Lakebase now includes high availability. A high availability endpoint pairs your primary read/write compute with one to three secondary compute instances across availability zones. If the primary becomes unavailable, Lakebase automatically promotes a secondary and your application continues from the last committed transaction, with no connection string changes. You can optionally serve read traffic from the secondary compute instances for read offload. See High availability.

Lakebase is now available in three additional regions:

• ca-central-1 (Canada Central)
• eu-west-2 (Europe – London)
• sa-east-1 (South America – São Paulo)

See Get started with Lakebase.

Lakebase is now generally available. GA includes autoscaling, scale-to-zero, instant branching, automated backups, point-in-time recovery, an adjustable storage quota, and expanded region availability.

See Get started with Lakebase Postgres.

Lakebase is now managed through the Lakebase App, accessed from the apps switcher in the Databricks UI. This replaces the previous workflow of navigating to the Compute tab in the Lakehouse UI.

Lakebase (Public Preview) now supports direct connections from the SQL editor with full read-write access. See Query from SQL editor in the workspace.

Lakebase now includes a Metrics dashboard in the Lakebase App for monitoring system and database health. Open Monitoring > Metrics to see graphs for RAM and CPU usage, connection counts, database size, compute cache hit rate, working set size, row operations, deadlocks, and replication delay. Select a branch, compute, and time range to scope the view. See Metrics dashboard.

Lakebase (Public Preview) now supports Access Control Lists (ACLs). Grant CAN CREATE or CAN MANAGE permissions to control who can access and manage project resources. Manage permissions from project settings in the Lakebase App. See Manage project permissions.

Lakebase is now in Public Preview on AWS. This new version of Lakebase introduces autoscaling compute, scale-to-zero, database branching, instant restore, and a redesigned project-based interface. To allow users to explore the new version, usage of Lakebase is free for a limited time. Billing for Lakebase usage begins in January 2026. See Get started with Lakebase Postgres.

Lakebase (Beta) is now available on AWS as a new app in the app switcher, separate from the existing Lakebase Public Preview. This new version introduces autoscaling, scale-to-zero, database branching, instant restore, and project-based organization with a redesigned UI.

See Get started with Lakebase Postgres (Beta).

The OLTP Database tab in the Compute section of the workspace is renamed to Lakebase Postgres. Use this tab to create and manage your Lakebase database instances.

See Manage projects.

You can now create synced tables in Snapshot sync mode from Iceberg tables or foreign tables.

See Serve lakehouse data with synced tables.

For newly created synced tables, TIMESTAMP types in source tables are now mapped to TIMESTAMP WITH TIMEZONE in synced tables. In existing synced tables, TIMESTAMP types continue to map to TIMESTAMP WITHOUT TIMEZONE.

See Serve lakehouse data with synced tables.

You can now tag a database instance and a synced table with a budget policy to attribute billing usage to specific policies. Additionally, custom tags can be added to a database instance for more granular attribution of compute usage to teams, projects, or cost centers.

See Manage projects, Serve lakehouse data with synced tables, and Custom tags.

You can now define Lakebase database catalogs, instances, and synced tables in a bundle. This means you can deploy a full database environment with one command instead of setting up resources separately.

When you deploy a bundle with a database instance, the instance starts running immediately.

See database_catalog, database_instance, and synced_database_table.

The Lakebase: Managed Postgres OLTP Database preview is now enabled by default. Users can start creating Lakebase database instances without needing workspace admins to enable this preview first. Admins can disable the preview if needed.

See Manage projects and Manage Databricks previews.

You can use the Databricks Terraform provider to create Lakebase resources such as database catalogs, database instances, and synced tables. This lets you set up and manage Lakebase resources with infrastructure as code.

See the Terraform documentation and changelog.

Synced tables are now automatically metered and billed. Synced tables use Spark Declarative Pipelines on Lakeflow to sync data from Unity Catalog tables into a Lakebase database instance. All usage is tracked and reported in system.billing.usage.

See Billable usage system table reference and Serve lakehouse data with synced tables.

The following supported Postgres extensions are now available at newer versions. Existing installations keep their current version until you update them with ALTER EXTENSION.

• postgis, postgis_raster, postgis_sfcgal, postgis_tiger_geocoder, postgis_topology, address_standardizer, and address_standardizer_data_us: updated to 3.3.10 on Postgres 16, 3.5.7 on Postgres 17, and 3.6.4 on Postgres 18.
• hll: updated to 2.21 on Postgres 16, 17, and 18.

See Update an extension version.

The new lakebase_tokenizer extension lets you control how Lakebase Search turns text into search terms, so keyword search matches the way your data and users actually read. Native PostgreSQL full-text search gives you little control over this step. lakebase_tokenizer adds a configurable whole-word tokenizer through PostgreSQL's standard text-search dictionary interface, with the controls that matter for relevance:

• Normalization: lowercase text, apply Unicode normalization for byte-stable matching, and strip accents so café matches cafe.
• Stopwords and synonyms: manage your own stopword and synonym lists in SQL, so terms like usa and united_states match the same rows.
• Stemming and possessives: reduce words to their root with a native English stemmer and strip trailing 's, so running matches run and John's matches john.

It produces standard tsvector values, so a tokenizer you configure works with to_tsvector, GIN (generalized inverted index) indexes, and the lakebase_text lakebase_bm25 index for BM25 (best match 25) ranking. Install it after enabling Lakebase Search in your project settings. See lakebase_tokenizer.

Lakebase captures its Postgres observability telemetry into the system.lakebase system tables, with no configuration to create and no destination to choose. Query plans, session history, wait events, schema-change history, database and compute health, and Postgres logs land there, and records are retained for 7 days. Account admins and metastore admins can read the tables and manage access to them, and a user with access queries the telemetry the way they would any Unity Catalog table. See Lakebase telemetry in system tables.

You can now manage Lakebase backup schedules programmatically. The backup schedule API lets you read and set a root branch's automated snapshot cadences (daily, weekly, or monthly) and how long each cadence's snapshots are retained. This API is in Beta and available to all Lakebase workspaces. See Create a backup schedule.

Lakebase Search is now generally available. It brings scalable vector and keyword search to Lakebase projects through two Postgres extensions, so you can run semantic and exact-term search in a single database without a separate search stack:

• lakebase_vector adds the lakebase_ann index type for approximate nearest-neighbor vector search. It is drop-in compatible with pgvector, using the same types, operators, and query syntax, and scales to over 1 billion vectors on a single index with builds up to 50-100x faster than HNSW.
• lakebase_text adds the lakebase_bm25 index type for BM25 full-text search. It works with standard tsvector types and operators, adding BM25 ranking and top-K pushdown that PostgreSQL's native GIN index does not provide.

Indexes are storage-backed, so they are available immediately after scale-to-zero without warmup. Combine both extensions for hybrid search that returns semantic and exact-term matches in one ranked list. To get started, enable Lakebase Search in your project settings, then install the extensions.

See Lakebase Search, lakebase_vector, and lakebase_text.

You can now manage Lakebase snapshots programmatically. The snapshots API lets you create, get, list, and delete point-in-time snapshots of a project branch, and restore a snapshot by creating a new branch from it. This API is in Beta and available to all Lakebase workspaces. See Create a snapshot.

Lakebase is now available in japaneast (Japan East). For the full list of supported regions, see Region availability. To get started, see Get a Postgres database.

Lakebase now provides a Service Level Agreement (SLA), with a 99.99% Monthly Uptime Percentage for Lakebase Endpoints configured in zone-redundant high availability mode and a 99.9% Monthly Uptime Percentage for Single Compute Lakebase Endpoints. For details, see Service Level Agreements (SLA) for Online Services.

Lakebase updated its supported Postgres versions to 16.15, 17.11, and 18.6. These minor releases bring you the latest upstream Postgres fixes and security patches. When a new minor version is available, Lakebase applies it the next time your compute restarts. See Manage updates.

A new Genie entry in the Lakebase project sidebar opens Genie Code, the agentic Azure Databricks assistant, scoped to your Lakebase project. Ask it in natural language to run multi-step data and AI tasks: explore your databases and tables, investigate and debug issues, and create and populate tables. It's the same Genie Code you use elsewhere in Azure Databricks, here working on your Lakebase project, and it's now available for all Lakebase projects. See Genie Code.

Lakebase now supports additional compliance security profile standards on Azure: ISMAP, IRAP (australiaeast), and UK Cyber Essentials Plus (uksouth, ukwest). See Compliance.

Lakebase is now available in four additional Azure regions: North Central US (northcentralus), France Central (francecentral), Germany West Central (germanywestcentral), and East Asia (eastasia). For the full list of supported regions, see Region availability.

Programmatic management of Lakebase Postgres is now generally available through the REST API, Databricks CLI, and Databricks SDKs (Python, Java, Go). These interfaces support operations for projects, branches, endpoints, databases, roles, credentials, synced tables, and catalogs. See Lakebase API guide.

Lakebase is now PCI-DSS and HITRUST compliant for workspaces with the compliance security profile enabled. On AWS, this support is limited to the us-east-1 region. On Azure, all regions where Lakebase is available are supported. See Data protection.

Lakebase is now enabled by default in workspaces with the compliance security profile, including workspaces that use the HIPAA, C5, or TISAX standard and workspaces that enable the profile without a compliance standard. You no longer need to enable Lakebase in these workspaces. See Data protection.

Lakebase adds AI-assisted troubleshooting: database issues and early warnings are surfaced to you with Insights, and you can then investigate and fix them conversationally with Genie. Both draw on database telemetry captured to Unity Catalog. See Observability.

Lakebase is now SOC 2 Type 2 compliant. For our SOC reports, please ask your Databricks account team or see Compliance.

To deliver Postgres version updates, security patches, and new features to your projects sooner, Lakebase has shortened the planned update notification window. Planned updates are now posted at least 3 days in advance, rather than 7. You can check your project's settings for upcoming updates.

See Manage updates.

Lakebase now supports Postgres 18. Postgres 17 remains the default version. To use Postgres 18, select it when creating a new project. For a full list of supported versions, see Postgres compatibility.

Lakebase Search brings scalable vector and keyword search to Lakebase projects through two new Postgres extensions, so you can run semantic and exact-term search in a single database without a separate search stack:

• lakebase_vector adds the lakebase_ann index type for approximate nearest-neighbor vector search. It is drop-in compatible with pgvector, using the same types, operators, and query syntax, and scales to over 1 billion vectors on a single index with builds up to 50-100x faster than HNSW.
• lakebase_text adds the lakebase_bm25 index type for BM25 full-text search. It works with standard tsvector types and operators, adding BM25 ranking and top-K pushdown that PostgreSQL's native GIN index does not provide.

Indexes are storage-backed, so they are available immediately after scale-to-zero without warmup. Combine both extensions for hybrid search that returns semantic and exact-term matches in one ranked list. To get started, enable Lakebase Search in your project settings, then install the extensions. See Lakebase Search, or read the announcement blog post.

Starting June 1, 2026, snapshot storage is billable for Lakebase. Snapshot storage size is shown on the Backup & Restore page. For pricing details, see the Lakebase pricing page. To learn about managing snapshots, see Snapshots.

New Lakebase projects now have native Postgres password authentication disabled by default. Existing projects are unaffected. To allow native Postgres roles to connect with static passwords, enable password connections in project settings. See Manage password connections.

Customer-managed keys (CMK) for Lakebase are now available in all Lakebase regions on AWS and Azure. CMK encrypts Lakebase project data at rest using a key your organization owns in its cloud KMS. CMK is available for new Lakebase projects. See Customer-managed keys for Lakebase.

Lakebase now supports compute sizes up to 64 CU (128 GB RAM), up from the previous maximum of 32 CU (64 GB RAM). Larger workloads can now use dynamic scaling. See Autoscaling.

When you delete a Lakebase project, it now enters a soft-deleted state for 7 days before permanent deletion. You can recover the project during that window, restoring all branches, databases, endpoints, and project settings. See Recover a deleted project.

New Lakebase instances now scale to zero by default after 24 hours of inactivity, reducing compute costs for idle projects. To learn more about scale to zero, see Scale to zero. To configure scale to zero, see Configure scale to zero. To change the scale to zero default for new Lakebase projects, see Compute defaults. This change does not affect existing projects.

You can now store every insert, update, and delete on your Lakebase Postgres tables as Unity Catalog managed Delta tables. Lakebase Change Data Feed (CDF) captures row-level changes from the write-ahead log for downstream pipelines, models, and audit, with no external CDC stack to run. See Lakebase Change Data Feed.

Azure Databricks is upgrading all Lakebase Provisioned instances to the Lakebase Autoscaling platform. Upgrades begin in June 2026 for customers who have requested them, with remaining instance upgrades continuing in the following weeks. Workspace admins will receive an email with upgrade dates before the upgrade begins.

The upgrade is automatic. Connections restart briefly during the transition, and existing connection strings, API calls, Declarative Automation Bundles, and Terraform configurations continue to work without modification.

After the upgrade, the following changes apply:

• Your instances will support autoscaling features and can be managed through both the new Autoscaling UI and the familiar Provisioned UI, which remains available until September 1, 2026.

• Each instance receives a new regional connection string that provides optimized ingress:

  • Existing connection strings: Provisioned connection strings (without a region) continue to work over the existing inbound Private Link and do not require Service Direct Private Link.
  • New regional connection string: If you use Private Link and connect to Lakebase from outside the Azure Databricks workspace, you must configure inbound Private Link for performance-intensive services to use the new regional connection string.

• To use new autoscaling features such as scale-to-zero in your Declarative Automation Bundles and Terraform configurations, update them to use autoscaling semantics.

• Lakebase GA pricing applies. With elastic compute replacing fixed-size instances, most customers will see a reduction in compute costs.

• The Forward ETL and REST API Private Preview features on Lakebase Provisioned are disabled after the upgrade. Their replacements, Lakebase Change Data Feed and the Data API, are available on the Autoscaling platform.

Lakebase Autoscaling adds autoscaling and scale-to-zero, point-in-time restore and snapshots, maintenance window scheduling, database branching, and other enhancements. For details about what to expect, what changes, and what actions to take, see Upgrade to Autoscaling.

To request an expedited upgrade or if you have questions, contact your account team or Azure Databricks Support.

You can now manage encrypted Lakebase project data using a customer-managed key (CMK) from your own cloud KMS. This gives your organization full control over encryption and helps meet regulatory and compliance requirements. See Customer-managed keys for Lakebase.

You can now add Lakebase databases as resources for your Databricks app. See Add a Lakebase resource to a Databricks app.

Lakebase now includes the following features:

• OAuth roles: Create OAuth roles using the Lakebase UI or REST API, in addition to SQL. See Create Postgres roles.
• Budget policies and tags: Associate a serverless budget policy and add custom tags to your project for cost attribution and tracking. See Manage projects.

New Lakebase instances are now created with autoscaling compute, scale-to-zero, branching, and instant restore by default. This applies when you create instances using the UI, Database instance API, Terraform, or SDKs. See Autoscaling.

Lakebase is now generally available for workspaces with the compliance security profile when the compliance standard is set to HIPAA, C5, TISAX, or None.

See Lakebase compliance.

Lakebase now includes high availability. A high availability endpoint pairs your primary read/write compute with one to three secondary compute instances across availability zones. If the primary becomes unavailable, Lakebase automatically promotes a secondary and your application continues from the last committed transaction, with no connection string changes. You can optionally serve read traffic from the secondary compute instances for read offload. See High availability.

Lakebase is now generally available on Azure, with autoscaling compute, scale-to-zero, branching, and instant restore. The GA release extends region support to 11 new Azure regions in addition to the existing eastus2, westeurope, and westus regions.

New regions:

• australiaeast (Australia East)
• brazilsouth (Brazil South)
• canadacentral (Canada Central)
• centralindia (Central India)
• southcentralus (South Central US)
• southeastasia (Southeast Asia)
• eastus (East US)
• centralus (Central US)
• northeurope (North Europe)
• uksouth (UK South)
• westus2 (West US 2)

See Get started with Lakebase.

Lakebase Postgres is now available on Azure in Beta. Access the Lakebase app from the app switcher to use autoscaling compute, scale-to-zero, database branching, and instant restore.

Supported Azure regions:

• eastus2 (East US 2)
• westeurope (West Europe)
• westus (West US)

See Get started with Lakebase.

Lakebase is now managed through the Lakebase App, accessed from the apps switcher in the Databricks UI. This replaces the previous workflow of navigating to the Compute tab in the Lakehouse UI.

The OLTP Database tab in the Compute section of the workspace is renamed to Lakebase Postgres. Use this tab to create and manage your Lakebase database instances.

See Manage projects.

You can now create synced tables in Snapshot sync mode from Iceberg tables or foreign tables.

See Serve lakehouse data with synced tables.

For newly created synced tables, TIMESTAMP types in source tables are now mapped to TIMESTAMP WITH TIMEZONE in synced tables. In existing synced tables, TIMESTAMP types continue to map to TIMESTAMP WITHOUT TIMEZONE.

See Serve lakehouse data with synced tables.

You can now tag a database instance and a synced table with a budget policy to attribute billing usage to specific policies. Additionally, custom tags can be added to a database instance for more granular attribution of compute usage to teams, projects, or cost centers.

See Manage projects, Serve lakehouse data with synced tables, and Custom tags.

You can now define Lakebase database catalogs, instances, and synced tables in a bundle. This means you can deploy a full database environment with one command instead of setting up resources separately.

When you deploy a bundle with a database instance, the instance starts running immediately.

See database_catalog, database_instance, and synced_database_table.

The Lakebase: Managed Postgres OLTP Database preview is now enabled by default. Users can start creating Lakebase database instances without needing workspace admins to enable this preview first. Admins can disable the preview if needed.

See Manage projects and Manage Azure Databricks previews.

You can use the Databricks Terraform provider to create Lakebase resources such as database catalogs, database instances, and synced tables. This lets you set up and manage Lakebase resources with infrastructure as code.

See the Terraform documentation and changelog.

Synced tables are now automatically metered and billed. Synced tables use Spark Declarative Pipelines on Lakeflow to sync data from Unity Catalog tables into a Lakebase database instance. All usage is tracked and reported in system.billing.usage.

See Billable usage system table reference and Serve lakehouse data with synced tables.