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 and locality groups 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.

"I rely on Atlas because I know it works and it gets the job done."

– Hicaro Adriano, Principal Software Engineer, Beck's Hybrids

Company Background​

Founded in 1937, Beck's Hybrids appreciates the farmers who have helped them grow to become the largest family-owned retail seed company and third-largest seed brand in the United States. This position gives Beck's access to the best genetics and trait technologies from suppliers worldwide. Beck's strives to provide all customers with the tools, support, and resources they need to succeed.

The Obstacle: Unreliable Migrations​

The Beck's Hybrids IT department began with a few engineers managing mainframes. Over the years, the development team has grown in an effort to provide customers and internal users with various applications that help power all aspects of the business. These systems include "FARMServer®", a precision farming platform that helps farmers make decisions based on optimal planting windows and growth stage modeling.

FARMServer is a complex, Microsoft SQL Server-based application that has grown to include a wide range of features and functionalities. Its codebase heavily utilizes stored procedures, views, and custom types to handle the intricate logic required for precision farming.

Beck's has traditionally developed its technologies in-house, and that includes their schema management system for these applications. Their system was a semi-automatic process that required manually written migrations, which can be quite fragile.

Their process required developers to manually track which revisions had been applied, carefully compare that to the current state in production, and then apply the remaining changes one-by-one - "hoping they wouldn’t fail." With no batch transactions or safety mechanisms in place, even a small mistake often left production in an inconsistent state.

Hey everyone!

We're excited to announce the release of Atlas v0.36 with a comprehensive set of new features and improvements that further strengthen Atlas as your go-to database schema management tool:

• Snowflake Driver Beta - Atlas now supports Snowflake databases in beta, expanding our data warehouse schema management capabilities.
• PostgreSQL Partitions - Declarative management of PostgreSQL partitions has long been a top community request, and we are excited to say it's now available!
• Azure DevOps Integration - Seamless CI/CD integration with Azure DevOps pipelines for database schema management.
• Google Spanner Beta - Beta support for Google Cloud Spanner, bringing Atlas to Google's horizontally scalable and globally distributed database.
• Datadog SIEM Support - Enhanced security monitoring with Datadog integration for audit logs and schema monitoring.
• ORM Schema Linting - Advanced schema validation and policy enforcement for all supported ORM integrations.
• Explain Pipelines Errors - New AI-powered error explanations to help you quickly understand and resolve deployment errors with Atlas Pipelines.

Atlas manages and migrates database schemas by following modern DevOps principles. This includes allowing teams to define and enforce custom rules for their database schemas to ensure consistency, compliance, and best practices throughout the development lifecycle.

Here's a breakdown of what Atlas Schema Rules are, why they are beneficial, and how to implement them:

As applications grow, their underlying database schemas inevitably grow more complex. What often starts as an afterthought handled by a single developer quickly turns into a critical, high-risk responsibility that demands precision and discipline.

Tools like Flyway and Liquibase automate the application of schema changes, but they stop short of addressing the real pain points: planning and validating those changes before they hit production. These steps remain largely manual, error-prone, and disliked by most developers.

Atlas is designed to fill this gap by automating the entire lifecycle of schema changes. Inspired by Terraform, Atlas provides a declarative approach to database schema management, enabling teams to define their schemas as code and automate the planning, validation, and application of changes.

Why Terraform for Databases?​

Infrastructure teams have standardized on tools like Terraform to manage cloud resources declaratively. Databases, despite being critical infrastructure, often remain outside this workflow. Schema changes are still handled manually or with ad-hoc migration scripts, leading to drift, unpredictability, and production risks.

Hey everyone!

It's been just over a week since our last release, and we are back with another batch of exciting features and improvements. Here's what's in store for you in Atlas v0.35:

• Bootstrap Projects - You can now bootstrap SQL projects with one command, making it easier to get started with Atlas. Using the new split and write template functions, you can now create a code representation of your database schema in SQL or HCL format to turn your database into code in no time.
• Atlas for Oracle in Beta - We are excited to announce that Atlas is now in beta for Oracle databases.