Reflecting On The Past 6 Years Of Data Engineering

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.

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