When you think about a data team, what comes to mind?

Maybe you think about a single group of developers along with some QA support.

But the truth is, today the lines of what consists of a data team are becoming more blurred.

Modern data engineering has evolved into a much more continuous and collaborative endeavor spanning across multiple teams.

Data is now often viewed as a product just like you might think about a software application.

And because of this shift, new approaches for building and releasing data products have evolved as well.

A term often used to describe this new workflow is “data ops”

And in today’s video I’m going to break down three of the biggest trends in this world of data ops to help you feel more aware and confident working in this new way.

▬▬▬▬▬▬ LET'S WORK TOGETHER ▬▬▬▬▬▬
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▬▬▬▬▬▬ T I M E S T A M P S ⏰ ▬▬▬▬▬▬
0:00 - Intro
0:47 - Automation
3:14 - Integration
5:29 - Collaboration

▬▬▬▬▬▬ Useful Links ▬▬▬▬▬▬
► https://docs.microsoft.com/en-us/azure/architecture/example-scenario/data-warehouse/dataops-mdw
► https://github.com/features/actions
► https://about.gitlab.com/stages-devops-lifecycle/continuous-integration/
► https://docs.docker.com/ci-cd/best-practices/
► https://www.terraform.io/
► https://airflow.apache.org/
► https://luigi.readthedocs.io/en/stable/#
► https://www.jenkins.io/
► https://medium.com/datareply/integrating-slack-alerts-in-airflow-c9dcd155105

▬▬▬▬▬▬ Want to learn more? ▬▬▬▬▬▬
Data-Build-Tool (dbt) ► https://bit.ly/3HCS0ht
Snowflake ► https://bit.ly/3mTpqk0
GitHub ► https://bit.ly/331QnuJ
Terraform ► https://bit.ly/3EV4wam
Power BI ► https://bit.ly/3JGErQ3

▬▬▬▬▬▬ Connect with me ▬▬▬▬▬▬
Twitter ► https://www.twitter.com/kahandata
Newsletter ► https://www.kahandatasolutions.com/blog
Website ► https://www.kahandatasolutions.com


3 Must-Know Trends for Data Engineers | DataOps
#kahandatasolutions #dataengineering #dataops