As applications evolve, database schema changes become inevitable. However, some types of changes can break the contract between the backend server and the database, leading to errors during deployment and application downtime. Ensuring that these breaking changes are detected and prevented is crucial for maintaining system stability and delivering a seamless user experience. In this post, we will explore how Atlas can help you prevent breaking schema changes from impacting your application.

It's been two months since the release of v0.9.0, so we figured it's about time to release a new version and share with you what we've accomplished so far, as well as what's to come in the upcoming weeks. Besides the many improvements and bug fixes in v0.10.0, we added two major features to Atlas that I want to share with you: schema loaders and the Community Preview of Atlas Cloud.

For a long time, one of the most common feature requests we've been getting from our users is the ability to manage their desired "schema state" using SQL. This is understandable, using Atlas DDL (HCL) can feel unfamiliar to some users, especially those who have never worked with Terraform before. For this reason, we're excited to announce the release of Atlas v0.9.0, which now fully supports SQL.

Learning new things​

One of my favorite things about software engineering, is that it's a career of continuous learning. Every new project or challenge presents us with an opportunity to increase our knowledge and improve our skills. Different people learn in different ways, but for me, one of the most effective ways to learn something new has always been by doing. Sure, reading books, watching videos and immersing myself in technical documentation is foundational, but I only feel like I really understand something after I've applied it in some practical way.

This is why I'm excited to announce that we've partnered with Wilco to create a new kind of learning experience for Atlas users. Wilco develops a platform, built to emulate the conditions at a tech startup, sends users on "quests" that cover everyday engineering tasks - from deploying an app to finding the root cause of a production issue - utilizing real-life tech stacks.

When I first heard about Wilco, I was immediately intrigued by the idea of using a simulation game to teach people about database migrations. Particularly interesting, is the possibility to present people with opportunities to solve all kinds of issues that many engineers only hit during a serious outage when there's very little room for error.

Atlas is most commonly used for managing and applying schema changes to databases, but it can also be used for something else: exploring and understanding database schemas.

With inspection, Atlas connects to your database, analyzes its structure from the metadata tables, and creates a graph data structure that maps all the entities and relations within the database. Atlas can then take this graph and represent it in various formats for users to consume. In this post, I will present two such forms of representation: Entity Relationship Diagrams (ERDs) and JSON documents.

Most software projects are backed by a database, that's widely accepted. The schema for this database almost always evolves over time: requirements change, features are added, and so the application's model of the world must evolve. When this model evolves, the database's schema must change as well. No one wants to (or should) connect to their production database and apply changes manually, which is why we need tools to manage schema changes. Most ORMs have basic support, but eventually projects tend to outgrow them. This is when projects reach to choose a schema migration tool.

Many such tools exist, and it's hard to know which to choose. My goal in this article is to present 3 popular choices for migration tools for Go projects to help you make this decision.

Introduction​

Today we are very excited to announce the release of Atlas Terraform Provider v0.4.0. This release brings some exciting new features and improvements to the provider which we will describe in this post.

In addition, this release is the first to be published under our new partnership with HashiCorp as a Technology Partner. Atlas is sometimes described as a "Terraform for Databases", so we have high hopes that this partnership will help us to bring many opportunities to create better ways for integrating database schema management into IaC workflows.

Wikipedia defines Multi-tenancy as:

a software architecture in which a single instance of software runs on a server and serves multiple tenants.

In recent years, multitenancy has become a common topic in our industry as many organizations provide service to multiple customers using the same infrastructure. Multitenancy usually becomes an issue in software architecture because tenants often expect a decent level of isolation from one another.

In this post, I will go over different known approaches for achieving multi-tenancy and discuss the approach we took to build Ariga's cloud platform. In addition, I will demonstrate how we added built-in support for multi-tenant environments in Atlas to overcome some of the challenges we faced.

With the release of v0.6.0, we introduced a workflow for managing changes to database schemas that we have called: Versioned Migration Authoring.

Today, we released the first version of the Atlas migration execution engine, that can apply migration files on your database. In this post, we will give a brief overview of the features and what to expect in the future.

Losing data is painful for almost all organizations. This is one of the reasons teams are very cautious when it comes to making changes to their databases. In fact, many teams set explicit policies on what kinds of changes to the database are allowed, often completely prohibiting any change that is destructive.

Destructive changes are changes to a database schema that result in loss of data. For instance, consider a statement such as:

ALTER TABLE `users` DROP COLUMN `email_address`;

This statement is considered destructive because whatever data is stored in the email_address column will be deleted from disk, with no way to recover it.