Data Engineering Podcast

Reflecting On The Past 6 Years Of Data Engineering

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Summary

This podcast started almost exactly six years ago, and the technology landscape was much different than it is now. In that time there have been a number of generational shifts in how data engineering is done. In this episode I reflect on some of the major themes and take a brief look forward at some of the upcoming changes.

Announcements
  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • Your host is Tobias Macey and today I'm reflecting on the major trends in data engineering over the past 6 years
    • Interview
    • Introduction
    • 6 years of running the Data Engineering Podcast
    • Around the first time that data engineering was discussed as a role
      • Followed on from hype about "data science"
      • Hadoop era
      • Streaming
      • Lambda and Kappa architectures
        • Not really referenced anymore
        • "Big Data" era of capture everything has shifted to focusing on data that presents value
          • Regulatory environment increases risk, better tools introduce more capability to understand what data is useful
          • Data catalogs
            • Amundsen and Alation
            • Orchestration engine
              • Oozie, etc. -> Airflow and Luigi -> Dagster, Prefect, Lyft, etc.
              • Orchestration is now a part of most vertical tools
              • Cloud data warehouses
              • Data lakes
              • DataOps and MLOps
              • Data quality to data observability
              • Metadata for everything
                • Data catalog -> data discovery -> active metadata
                • Business intelligence
                  • Read only reports to metric/semantic layers
                  • Embedded analytics and data APIs
                  • Rise of ELT
                    • dbt
                    • Corresponding introduction of reverse ETL
                    • What are the most interesting, unexpected, or challenging lessons that you have learned while working on running the podcast?
                    • What do you have planned for the future of the podcast?
                      • Parting Question
                      • From your perspective, what is the biggest gap in the tooling or technology for data management today?
                        • Closing Announcements
                        • Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
                        • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
                        • If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
                        • To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

                          • The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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                          Data Engineering PodcastBy Tobias Macey
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