4 posts tagged with "case study"

"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.

"When I came across Atlas and saw it described as Terraform for your database, it immediately resonated. That’s exactly what we needed. Just like Terraform solved our AWS problems, we needed something to bring that same level of control to our data."

– Maciej Bukczynski, Director of Technology, Darkhorse Emergency

Company Background​

Darkhorse Emergency is a SaaS decision analytics platform for public safety services, primarily fire departments, that uses data and predictive analytics to optimize operations and resource allocation. Their platform allows for decisions to be simulated and assessed before being made, creating more transparency amongst public service teams and those that depend on them.

The Bottleneck: Evolving a Logic-Heavy Postgres Schema​

"For us PostgreSQL isn't just storage. It's the core of our business logic. "

Darkhorse Emergency's platform is built on a complex PostgreSQL database that serves as the backbone for their application. It is an elaborate system that processes many types of data, including 911 calls, census reports, and other public data sources.

By maintaining a carefully designed chain of views, functions, custom types, and triggers, the team is able to offload complex calculations and logic to the database. This ensures that their application can efficiently handle the demands of public safety services. "For us PostgreSQL isn't just storage. It's the core of our business logic," said Maciej Bukczynski, Director of Technology at Darkhorse Emergency.

However, this complexity presents a significant challenge when it comes to evolving the database schema. With so much happening within the database itself, the team very quickly ran into the limitations that come with common migration tools. "For example, we might have a view that feeds into 50 other views; if we want to make a change to that, we need to carefully recreate dependencies and ensure that everything remains consistent", Bukczynski explained.

The team initially tried to use classic migration tools like Flyway and Liquibase, but found that manually planning and applying migrations in such an intricate system was not only time-consuming but error-prone.

Prepared for an Atlas Community Webinar, October 2024

Introduction​

In recent years, the shift to declarative resource management has transformed modern infrastructure practices. Groundbreaking projects like Terraform, for infrastructure as code, and Kubernetes, for container orchestration, have exemplified the power of this approach. By focusing on what the end state should be rather than how to achieve it, declarative methods make systems more scalable, predictable, and easier to maintain—essential qualities for handling today's complex environments.

However, when it comes to managing database schemas, the industry has been slow to adopt declarative workflows. Atlas was created almost four years ago to address this gap.

Atlas supports two kinds of database schema migration workflows:

• Versioned Migrations - each change to the database is described as a migration script, essentially a SQL file containing the SQL commands to apply the change. Migrations are versioned and applied in order.

  Contrary to most existing migration tools, Atlas relies on users defining the desired state of the database schema in code Atlas generates the necessary migration scripts to bring the database to the desired state.

• Declarative Migrations - the database schema is described in a declarative way, and changes are applied by comparing the desired schema with the current schema and generating the necessary SQL commands to bring the database to the desired state.

To date, most teams that used Atlas in production have used it's versioned migration workflow which synthesizes the simplicity and explicitness of classic migration tools with the benefit of automated migration generation.

Recent improvements to Atlas have addressed many of the challenges and concerns teams have expressed around using declarative migrations in production in the past. In this post, we'll take a deep dive into the declarative migration workflow

"Everything on Atlas is just making too much sense for us."
— Kaushik Shanadi, Chief Architect

Conceal, a cybersecurity company, creates a secure browsing experience using a browser extension. With a lean engineering team, When Conceal shifted from serving individual consumers to working with managed service providers (MSPs), their clients' security requirements drove the need for a robust, multi-tenant architecture to ensure data isolation and scalability.

Kaushik Shanadi, VP and Chief Architect, led the charge in finding that solution.