Release Notes for 1.27 Release - September 2026

Calibo Accelerate now supports Docker deployment mode for deploying Web/API technologies on Google Cloud Virtual Machines.

You can configure the Google Cloud Project, Virtual Network, and VM Instance Type in the platform’s Cloud Configuration section. The same configuration can be used in the Deploy stage.

See Google Cloud Platform (GCP) section in Configure Connection Details of Cloud Service Provider Accounts.

The Data Profiler Dashboard now includes a Distinctness chart that displays the distinct values for each column as a bar graph. The bar colors indicate the distinctness level based on the configured thresholds.

This helps you quickly identify columns with a high proportion of repeated values without manually inspecting the data.

See Distinctness.

Audit history is now available for Snowflake and Databricks Data Profiler jobs. You can view logs for operations such as create, update, delete, run, and completion, as well as scheduled profiler job operations.

See View Data Profiler Audit Logs section of Data Profiling of Existing Data

Calibo Accelerate supports role-based access control (RBAC) for data quality through the Data Engineer role. Users with this role can create, run, and promote pipelines; view pipelines and dashboards; edit pipeline configuration; and cancel pipeline runs.

This enhancement provides controlled access to Data Quality capabilities, helping organizations maintain governance and manage permissions across Data Quality workflows.

See Role-Based Access Control (RBAC) for Data Quality

The currently Published MVD version is now automatically marked as the Active version. Lower versions of an MVD cannot be published.

This update improves version traceability and simplifies compliance auditing.

See Publish Minimum Viable Data (MVD).

An anomaly trend chart is displayed for every pipeline run, regardless of whether an anomaly is detected in the latest run. When the current run has no anomaly, the chart continues to display historical anomaly trends, if available. You can accept or reject a historical anomaly.

This enhancement provides continuous visibility into anomaly trends and enables users to monitor historical data quality behaviour, even when the latest pipeline run is normal. It also improves the quality of feedback available for future model evaluation and retraining.

See Anomaly Detection Results.

Calibo Accelerate now provides AI-assisted metadata generation for data assets. AI can generate metadata based on available dataset schema and user prompts, reducing manual effort and improving metadata completeness.

See Generating AI Metadata for Data Assets.

Calibo Accelerate now supports Google Cloud Platform (GCP) as a cloud provider when deploying JupyterLab using Docker. You can select GCP as the cloud provider during deployment and access the deployed JupyterLab environment for data analysis and data science use cases.

Support for New Technologies

Support for the following latest versions of technologies is now available on the Calibo Accelerate platform:

• Python 3.14

• Angular 22

• Node.js 26 with Express

• Node.js 26 with TypeScript without Express

• Node.js 26 without Express

• Core Java25 -Gradle

• Core Java25 -Maven

• Java25 Spring Boot 4.1-Gradle

• Java25 Spring Boot 4.1-Maven

• Java25 with GraphQL Spring Boot 4.1-Gradle

• Java25 with GraphQL Spring Boot 4.1-Maven

Deprecated technologies

The following End-of-Life (EOL) versions of technologies are no longer supported in Calibo Accelerate:

• Java 17 Spring Boot 3.5 – Gradle

• Java 17 Spring Boot 3.5 – Maven

• Java 17 with GraphQL Spring Boot 3.5 – Gradle

• Java 17 with GraphQL Spring Boot 3.5 - Maven

• Java 21 Spring Boot 3.5 – Gradle

• Java 21 Spring Boot 3.5 – Maven

• Java 21 with GraphQL Spring Boot 3.5 – Gradle

• Java 21 with GraphQL Spring Boot 3.5 – Maven

• Angular 19 

See Tools and Technologies Integrated with Calibo Accelerate Platform.