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 lays out how data version control is the most obvious next step in moving data pipelines from something that’s “maintained” to something that’s “engineered.” I enjoyed this blog post and I like many of the analogies that Gross draws, but I’ve encountered this idea of “git for data” in a
…Category: Software Engineering
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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Data warehouses are not just for business intelligence (BI) anymore. You can maximize the value of your data engineering, data science, and analytics work by investing in building out a
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