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Data Engineers Shouldn't Write Reverse ETL: A Guide to Building a Happy Data Engineering Department

This guide sheds light on the state of data engineering in 2021 as well as talks about the rise of Reverse ETL as a core component of the Modern Data Stack.

Pedram Navid.

Pedram Navid

March 23, 2021

10 minutes

Data Engineers Shouldn't Write Reverse ETL: A Guide to Building a Happy Data Engineering Department.
  • Extracting data defined in a model from the data warehouse
  • Figuring out what has changed since the last sync
  • Translating that from the model to conform to the various models of different APIs
  • And making sure that data is sent correctly and only once to the downstream systems
  • One can build these integrations internally but to paraphrase what Jeff Magnusson once said, “engineers should not be writing Reverse ETL”.

    Nobody likes to build integrations

    Maintaining data integrations with third-party services seems simple on the surface but is far from it -- managing APIs, keeping up with API changes, dealing with errors and retry logic, and accommodating an ever-increasing SaaS landscape. It is time-consuming, not rewarding, and leads to frustration for engineers

    Engineers don't want to write ETL jobs, and they don't want to write Reverse ETL jobs either. Period.

    Enter Hightouch!

    Built by former Segment employees, Hightouch offers a simple interface to integrate data from your warehouse with third-party SaaS tools using the same SQL-powered logic that powers your business.

    Let Hightough’s team of engineers solve data integration woes so that the suffering starts and ends with them, and your engineers can focus on solving problems core to your business.

    Freedom isn't limited to just engineers -- folks in operations, marketing, and sales are no longer constrained by the availability of engineers to provide superior customer experiences by having access to customer data in the tools they use everyday.

    Conclusion

    At Hightouch, we see Reverse ETL not only as the next logical step in the evolution of the modern data stack, but also as part of an acknowledgement that we need to design our systems to best support the people who work on them.

    The more we can move tedious, uninspiring work away from the hands of highly-skilled and creative people, and the more we can enable them to do what they do best, the better it is for the companies we run.

    What makes great analytics engineers is empathy for users and the ability to build meaningful data models that help drive business value. Nobody said that it involves maintaining CI pipelines and dbt has done a great job at offloading that concern through its cloud offering.

    Similarly, what makes a great data engineer is a passion to build useful frameworks and abstractions that help others do their jobs more efficiently without having to worry about the underlying infrastructure. It involves maintaining brittle Reverse ETL pipelines from a data warehouse to third-party data providers -- said nobody.

    Hightouch aims to offload that concern so that data engineers can do what they do best, nothing less.

    More on the blog

    • What is Reverse ETL? The Definitive Guide .
  • Friends Don’t Let Friends Buy a CDP.
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