Reflecting On The Past 6 Years Of Data Engineering
Data Engineering Podcast
Shownotes Transcript
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)
Sponsored By:
- Materialize): [
Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use.
Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features.
Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.
Go to materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access))