In the past few years, much has been written about problems like discoverability, observability, data quality, and the need for data teams to become more “engineering oriented” in their mindset. Movements like analytics engineering and open source tooling like dbt, Dagster, and Great Expectations have done an amazing job arming data practitioners with the tools that they need to start adopting the best practices of software engineering like modularity, testing, and release management. This shift in mindset has resulted in
…Tag: Software
There’s been a lot of discussion lately about systems for doing version control for data. Most recently, Ryan Gross wrote a blog post “The Rise of DataOps” where he
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The landscape of the data and analytics world is shifting rapidly. In many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing. This change has
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I have spoken to many fellow analytics practitioners who are adament that they want their team to never touch “production.” While there are good reasons to be careful whenever you make changes
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Often, Data and Analytics teams go under-utilized in their organization because they can not collaborate effectively with the broader Technology and Software Engineering teams.
By designing software following the “code
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