As of April 2026, MySQL 8.0 has reached End of Life (EOL), marking a critical security and operational milestone. When a database reaches EOL, the safety net is pulled away. Oracle will no longer release security patches, bug fixes, or performance improvements for the community edition. If a vulnerability is discovered tomorrow, your 8.0 instances will remain exposed.

In this post, we’ll explore what this means and how cloud platforms are responding to it.

As AI agents become staples in modern development workflows, automating everything from code reviews to complex schema migrations, the industry is hitting a critical inflection point. While the productivity gains are undeniable, the risks have become equally prominent. We are now seeing a recurring cycle of horror stories where unchecked agents inadvertently wipe production databases or trigger catastrophic outages.

The momentum of AI integration isn't slowing down, but the margin for error has vanished. To keep pace without compromising integrity, engineering teams must move beyond blind trust and implement robust safeguards to defend their infrastructure against "rogue" AI behavior.

To truly neutralize the risk of rogue AI, teams must pivot to Policy as Code. By codifying your governance directly into CI/CD pipelines, you shift from hoping for compliance to guaranteeing it, ensuring that every schema change is programmatically validated before it ever touches production.

Hey everyone!

We're excited to announce Atlas v1.2. This release brings column-level data lineage to Atlas Cloud, registry backups to your own cloud storage, a schema ownership policy for CI, and expanded database coverage.

Here is what you can find in this release:

• Column-Level Data Lineage - Trace how columns are derived from upstream sources across tables, views, and datasets in Atlas Cloud.
• Offline Access & Registry Backups - Back up Atlas Registry repositories to S3, GCS, or Azure Blob Storage. Atlas Pro license grants are cached in CI/CD environments, so your pipeline never has a single point of failure.
• Schema Ownership Policy - Enforce which GitHub users and teams can modify specific schema objects, closing the gap between CODEOWNERS and DDL access control.
• Database Driver Improvements - PostgreSQL routine permissions, user-mapping, and default ACLs; Snowflake tasks and pipes; Oracle UDTs; Expanded permissions for MSSQL, MySQL, and ClickHouse.

We're excited to announce Atlas v1.1.

This release delivers on a promise we made in v1.0: Database Security as Code is now available for Atlas Pro users.

We're also shipping declarative data management for lookup tables and seed data, expanding database coverage with Aurora DSQL, Azure Fabric, and CockroachDB Cloud, and further improving our drivers and Atlas Cloud.

Here is what you can find in this release:

• Database Security as Code - Manage roles, users, and permissions declaratively across various popular Atlas-supported databases.
• Declarative Data Management - Define lookup tables and seed data as code using various sync modes.
• Aurora DSQL Support - Manage schemas on AWS's new serverless distributed SQL database.
• Azure Fabric Support - Use Atlas to manage your Microsoft Azure Fabric databases.
• Spanner Enhancements - Added support for Spanner's PostgreSQL dialect and vector indexes.
• ClickHouse Improvements - Cluster mode and data retention on table recreation.
• CockroachDB Support - Full Atlas support for CockroachDB Cloud and self-hosted deployments.
• PostgreSQL Enhancements - CAST definitions and replica identity support.
• Slack Integration - Connect to our native Slack app for real-time notifications.
• Schema Exporters - Declarative export for schema inspection, drift and diff reporting, and more.

We're excited to announce Atlas v1.0 - just in time for the holidays! 🎄

v1.0 is a milestone release. Atlas has been production-ready for a few years now, running at some of the top companies in the industry, and reaching 1.0 is our commitment to long-term stability and compatibility. It reflects what Atlas has become: a schema management product built for real production use that both platform engineers and developers love.

Here's what's in this release:

• Monitoring as Code - Configure Atlas monitoring with HCL, including RDS discovery and cross-account support.
• Schema Statistics - Size breakdowns, largest tables/indexes, fastest-growing objects, and growth trends over time.
• Declarative Migrations UI - A new dashboard for databases, migrations, deployments, and status visibility.
• Database Drivers - Databricks, Snowflake, and Oracle graduate from beta to stable; plus improvements across Postgres, MySQL, Spanner, Redshift, and ClickHouse.
• Deployment Rollout Strategies - Staged rollouts (canaries, parallelism, and error handling) for multi-tenant and fleet deployments.
• Deployment Traces - End-to-end traceability for how changes move through environments.
• Multi-Config Files - Layer config files with -c file://base.hcl,file://app.hcl.

Hi everyone!

We're excited to share with you the release of Atlas v0.38, filled with many new features and enhancements for you to try.

• Oracle Triggers and Views - We've expanded the support for Oracle schemas to include triggers and views.
• Snowflake Additions - Our library of supported resources for Snowflake has also expanded with the additions of stages, external tables, hybrid tables, and dynamic tables.
• Google Spanner Additions - Spanner users can now manage geo-partitioning placements, locality groups, sequences, and change streams with Atlas.
• Expanded Analyzer Detection - Our linting analyzers now detect SQL injections in SQL schema and migration files, and incorrect usage of transactions in migration files.
• HTTP Data Source - Users can now use HTTP endpoints as data sources in the Atlas configuration file.
• PII Detection - Objects containing potentially sensitive or PII data can now be automatically or manually tagged in the Atlas Registry.
• Pre/Post-migration Hooks - Pre- and post-migration hooks enable teams to run custom logic before and after applying migrations.
• Atlas Monitoring - The Atlas Agent can now automatically discover and monitor RDS instances across multiple AWS accounts using IAM role assumption.
• Azure DevOps Repos CI/CD Integration - Atlas now provides native integration with Azure DevOps Pipelines and Azure Repos, including a dedicated Azure DevOps extension for seamless database schema CI/CD workflows.

Hey everyone!

Some time has passed since our previous release, and we're very excited to bring you another large batch of exciting additions in Atlas v0.37.

• Databricks Driver Beta - Atlas now supports managing Databricks databases in beta.
• ClickHouse Support Additions - We've expanded the support for ClickHouse to include clusters, user-defined functions, table projections, table partitions, and experiment types.
• SQL Server Support Additions - Our support for SQL Server has been extended to include SQL Server 2008, 2012, 2014, and 2016.
• Broader Scope for Linting Analyzers - Atlas now supports configuring analyzers to follow object deprecation workflows, enforce checks, block nolint usage, and allow or block specific SQL statements in migrations.
• Custom Migration Rules - Similar to custom schema rules, Atlas Pro users can now write rules for schema changes in their migrations.
• Pre-Execution Checks for Versioned Migrations - Added support for policy rules that run before migration execution. Teams can now allow or deny migrations based on conditions such as the number of pending files or specific SQL statements (e.g., blocking CREATE INDEX during peak hours).
• Cloud Databases as a Data Source - Users can now dynamically retrieve the migration status of different environments using the cloud_databases data source.
• Support for Hashicorp Vault - Atlas Pro users can now retrieve database credentials stored in Hashicorp Vault.
• Discover Database Instances for Schema Monitoring - Use the Atlas Agent to discover all database instances in your environment automatically in order to monitor them in Atlas Cloud.
• Protected Flows by Default - Atlas Cloud users can configure their settings to enable protected flows on all new projects.

Company Background​

Yad2 is the most popular online marketplace in Israel for buying and selling second-hand items. Since launching in 2005, Yad2 has offered an organized platform for the sale of various goods, including vehicles, housing, rentals, furniture, electronics, and more. With millions of users and listings, Yad2 handles a significant amount of data and requires a robust database management system.

Snowflake's cloud data platform has transformed data warehousing, yet many teams still manage schema changes using manually-composed SQL scripts and verification processes. As data teams grow and pipelines become more complex, these approaches often become more challenging to maintain and much riskier to use.

Schema changes in production environments can quickly lead to unexpected behavior and inconsistent data, and having more contributors increases the risk of human error leading to costly downtime or data integrity issues. These cases are familiar to many data teams because the traditional manual database deployment methods come with implicit risks.

To address these challenges, Snowflake teams have begun adopting tools to automate schema changes, enforce safety checks, and ensure consistent deployments across environments.

In this post, we will compare three popular Snowflake schema management tools – Atlas, schemachange, and SnowDDL – and guide you in building reliable CI/CD pipelines to deploy schema changes with more confidence and control.

AI agents are becoming a core part of daily development. We utilize them to help us write code, fix syntax errors, and perform tasks that speed up routine work. However, when it comes to high-risk operations like database schema changes, we are more hesitant to hand off control.

If you're currently partaking in the online conversation around AI agents, you have likely seen many posts like this where an AI agent executed improper schema changes or, in the case of our vibe coder, deleted whole databases.

While the AI agent can generate migrations and provide suggestions, it’s important to ensure these operations are performed safely.

Atlas is a database schema management tool that ensures safe and reliable schema changes. Users define their schemas as code, and Atlas performs migrations based on changes to these code definitions. With Atlas, you can configure lint checks, pre-migration validations, and schema testing, making it an ideal counterpart for AI agents.

In this post, we'll show you how to configure popular AI agents to work with Atlas to ensure that schema changes made by the agent are secure.