5 Things Every Data Engineer Needs to Know About Data Observability

5 Things Every Data Engineer Needs to Know About Data Observability

How to your data workflows, one pipeline at a time. This post is 5 Things Every Data Engineer Needs to Know About Data Observability

As a new or aspiring data engineer, there are some _[**_essential technologies and frameworks**](https://www.jesse-anderson.com/2020/12/data-engineering-technology-tree/)_ you should know. How to build a data pipeline? Check. How to clean, transform, and model your data? Check. How to prevent broken data workflows before you get that frantic call from your CEO about her missing data? Maybe not._

By leveraging best practices from our friends in software engineering and developer operations (DevOps), we can think more strategically about tackling the “good pipelines, bad data” problem. For many, this approach incorporates observability, too.

Jesse Anderson, managing director of Big Data Institute and author of Data Engineering Teams: Creating Successful Big Data Teams and Products, and _Barr Moses, co-founder and CEO of Monte Carlo, share everything you need to know to get started with this next layer of the data stack._

data-engineering data devops data-science data-observability

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