dbt vs. Liquibase

dbt (Data Build Tool) is best suited for doing the data transformation. dbt is just a transformation tool and it is not suitable for building a data pipeline which requires extraction of data and loading. dbt is well suited for SQL based transformation logic and it is less appropriate when transformation logic requires python.

Incentivized

Any Codebase that does schema or table changes all the time for development or where Development and code is mostly in the database or SQL liquibase is a must. In a codebase where the database is pretty static or is just a place to dump data, liquibase is probably too much. You also need to have a team for it to really make sense. Doing a solo or small team project doing full version control on the database is probably more overhead than it is worth.

Incentivized

• user experience makes it easy to work with SQL and version control
• customer success team and the dbt (data build tool) community help establish best practices
• thorough and clear documentation

• Liquibase provides a clear error log that allows us to pinpoint what to troubleshoot.
• The service does not deploy bad data, which helps us keep our databases clean.
• Liquibase works with GitHub Actions as well as AWS Codebuild and AWS Lambda. The flexibility allows us to deploy the service in many different ways.

Incentivized

• Slow load times of the dbt cloud environment (they're working on it via a new UI though)
• More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros

Incentivized

• Reducing Compatibility issues, when we upgraded Liquibase from 4.2 to 4.9. The same changeset which we were able to run on successfully using 4.2, part of it was now failing when tried to deploy using 4.9
• We are not able to see detailed logs (for different changes) in uDeploy when deploying changes through Liquibase
• Liquibase should rollback the if any one of the changes fails.

Incentivized

We are and will continue using Liquibase and it has become an integral part of our portfolio offering, any new product is by default adopting Liquibase stack.

Incentivized

dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.

Incentivized

the database deployments helped teams with a increased productivity,
faster delivery and low risk.

Incentivized

Liquibase's customer support team has been very instrumental in helping us drive the whole Database CI/CD initiative. We have always received very quick resolution to our queries or any roadblock we hit. Right from setting up Liquibase in our environment to this date the Liquibase team has always helped us deliver quality and innovative solutions.

Incentivized

Build process takes a toll.

Incentivized

Matillion is graphical versus dbt, which is SQL code-based (that, of course, is a matter of personal preference and not an objective advantage). The integrated testing, documentation generation, lineage, etc., were additional criteria that led us to choose dbt.

Incentivized

In my previous project and organization I have used Flyway for database change management and version control similar to Liquibase which I am currently using. Comparing it with Flyway, Liquibase provides more feature flexibility and enhancements to handle complex workflows with rollback capability and its usage of contexts and labels allow us to target changes to specific environments, which Flyway doesn’t support natively. Also Liquibase provides way to compare different schema and generate changelogs for syncing environments automatically where in it allows to have declarative schema management by using XML/YAML/SQL script format.

Incentivized

• In 3 months we re-wrote the data warehouse (15-20 sources) in dbt with 3 developers.
• We are using it continually for the past year with no issues.
• Sorry, I don't have ROI numbers but the impact was huge.

Incentivized

• We are still in the early phases, where the costs are potentially greater than the benefit. Trying to get Liquibase integrated into a pipeline has taken time investment and required some trial and error.
• We are still a relatively small shop with a relatively small number of schema changes (perhaps 1 every week or so). As such, we aren't at a place where we couldn't have managed control of this without a tool. However, there is no doubt that investing in a tool at this stage was the right move. Now we have established guidelines and a pattern for how to do schema changes in a way that will make things easily scalable as we continue to grow.

Incentivized