Version 4.9.0

Date: 15 November 2025

• Enhanced Secure Connectivity with Cloudbridge: Acceldata introduces Cloudbridge, delivering secure, private, and encrypted communication between the control plane and customer data planes—without requiring any inbound (ingress) network access. The new mTLS-based tunnel strengthens identity verification, simplifies connectivity, and improves security for hybrid and multi-cloud environments. For more information, see Cloudbridge.
• Query Lineage Enhancements: The Query Lineage framework has been refactored to improve scalability and reliability. Job orchestration is now managed from the Control Plane for better scheduling and monitoring. Users can also upload SQL files for lineage extraction while create a Query Lineage, removing payload size limits and enabling support for complex queries. For more information, see Query Lineage.
• Fivetran Integration: ADOC now supports Fivetran as a data source, enabling users to visualize replication pipelines and lineage between source and destination systems. The integration provides column-level lineage, metadata insights, and synchronization aligned with Fivetran’s sync schedule for improved transparency and governance. For more information, see Fivetran.
• Snowflake Materialized Views in Lineage: Lineage now supports materialized views and dynamic views as separate nodes. Users can easily trace how data flows between base tables, views, and materialized views—providing a more complete and accurate picture of data movement and transformations across their Snowflake environment. For more information, see Snowflake.
• Tableau Lineage Enhancements: Added support for Automatic lineage discovery across Tableau’s internal and external assets. Using Tableau’s GraphQL Metadata APIs, users can view complete data flows from external systems like Snowflake and BigQuery through Tableau Flows, data sources, and dashboards—delivering true end-to-end lineage visibility. For more information, see Data Lineage for Tableau. For more information, see Data Lineage for Tableau.
• Publish Good/Bad Records to Databricks: Now support publishing validated and failed records directly to Databricks using the Pushdown Engine, providing faster execution and deeper data quality visibility.
• Freshness Policy Revamp: Redesigned Freshness Policy framework with Pushdown Engine integration, enabling faster, more reliable freshness computation directly within target systems. Users can now run freshness policies on demand or through custom schedules, with improved flexibility and granular control. For more information, see Data Freshness Policy.
• Collibra Integration: ADOC now supports integration with the Collibra Data Catalog using either Basic Authentication or OAuth. Users can view basic data reliability and quality scores from the ADOC platform directly within Collibra. For more information, see Collibra.

• Fixed an issue where ZIP file uploads failed during policy import on Chrome and Edge browsers in Windows OS due to differing file type recognition between Windows and Mac; updated handling to support both formats.
• Fixed an issue where the Synapse crawler failed to fetch metadata for tables containing special characters (such as single quotes) in table names, by updating query handling to correctly escape special characters and ensure successful catalog updates.
• Addressed a security vulnerability where standalone data plane pod logs exposed sensitive information (such as access keys and secret keys) in debug mode by masking confidential data to ensure no sensitive details appear in logs.
• Fixed an issue where manually created Data Freshness policies on Databricks assets were not executing as expected; the problem has been resolved as part of the Custom Freshness Policy Revamp in this release.
• Fixed an issue where Data Quality policies allowed duplicate rules within a rule set, causing conflicts and save errors (“One rule per column is allowed”); updated validation logic to prevent duplicate measurement types on the same column, ensuring consistent rule execution across all environments.
• Fixed an issue where Data Reliability Performance Trends displayed scores offset by one month; updated event frequency handling to correctly align trend data with the selected time period, ensuring accurate month-to-date reporting.
• Fixed an issue where users with view-only permissions for Asset Settings were still able to edit and save changes; implemented backend and UI fixes to enforce proper permission checks, ensuring users with view access cannot modify settings.
• Fixed a login issue caused by duplicate local and SCIM accounts due to email verification and sync conflicts.
• Fixed mismatched success record counts between ADOC and legacy systems during migration by addressing discrepancies such as warning row handling and sample records.
• Fixed an inconsistency in Snowflake Pushdown where the persistence folder prefix did not generate the expected folder structure in S3; updated the path format to match Spark engine behavior by including <Persistence Folder Prefix Value>_<Unix_Timestamp_in_Milliseconds> for consistent data organization.
• Fixed an issue in Snowflake Pushdown where email notifications were not triggered for policies with Errored or Warning statuses due to incorrect resultStatus being set to SUCCESSFUL.
• Fixed an issue where the Asset Details page displayed incorrect columns in sample data after executing an SQL-based policy; updated logic to ensure sample data retrieves columns from the original table schema rather than the SQL policy view.
• Fixed an issue causing Data Quality and sample data failures on specific Hive tables due to schema conversion mismatches between Hive table definitions and underlying Parquet data; resolved by setting spark.sql.hive.convertMetastoreParquet to false for affected assets, ensuring successful profiling and policy execution.
• Fixed an issue where additional tags were not visible after adding more than five tags to a policy; updated the UI to ensure all tags are displayed when clicking the +More icon on both the Manage Policies and View Policy pages.
• Fixed an issue where users could select columns with invalid data types when creating Freshness rules, causing runtime Java exceptions; the UI now restricts column selection to valid types (date, datetime, or string) to prevent execution failures.
• Fixed inconsistent sorting on the Upcoming Jobs page by ensuring ascending/descending sorting is applied to the entire job list rather than only the visible page, allowing users to correctly view the top results based on previous or upcoming fire times regardless of page size.