Thanks for joining us at! We are a community of data engineers, architects, analysts, and data scientists who have all been burned by slow development cycles, broken pipelines, and inaccurate results. But we’ve also seen data at its best. With backgrounds at some of the largest data companies - Google, Twitter, Square, Salesforce, Cloudera, Informatica, and others - we’ve experienced firsthand how powerful getting the right data into the right hands fast can be. Figuring out how to make this experience the norm at every company is what attracted us the DataOps movement, which promises speed, quality, and flexibility as we work with data together.

DataOps itself is still nascent. We created this space where we can all learn from each other and share what’s worked and what hasn’t as we all embark on this DataOps journey. We’ll be sharing our experiences, considerations for your teams, best practices and frameworks that we’ve seen be successful, as well as other tips and tricks. But this is a new and changing area, and we by no means claim to have all the answers. We want to hear from you on what you’re most interested in and what’s worked for your teams. We hope you find this helpful and we can’t wait to hear what you’ve done with your data!


Breaking Down DataOps

DataOps has been interesting for us to see develop and evolve within all sorts of different companies. To understand why DataOps is such a big deal right now, it’s important to understand what problems it’s trying to solve…

Is DataOps Related to DevOps?

Just like how DevOps changed the way we develop software, DataOps is changing the way we create data products. But while they are both based on agile-frameworks, there are some key differences to keep in mind when comparing the two…


What Does DataOps Mean to Me? — Data Scientist Edition

Considerations for Pipeline Optimization within DataOps

We explore the implications of DataOps for data engineering as it relates to optimizing pipelines that feed data to enterprise data analytics and discovery products.

This is the first of a series of exploratory blog posts on DataOps practices and frameworks, where we will discuss what DataOps could mean to different roles in an organization.