Most Data Teams Are Doing It Wrong
Notas do episódio
Most data teams think they’re building value. In reality, they’ve become ticket queues.
In this episode, Chris Gambill explains his storied career in tech and data through the years, dealing with data at Fortune 500 company scale, and breaking out on his own.
We cover career growth, what separates senior engineers from true strategic operators, and the biggest mistakes people make early on. We discuss the classic problems that have plagued data teams for decades and why it’s all still a struggle.
Today’s podcast is sponsored by Estuary.
Without them, content like this isn’t possible. The best way to support this Newsletter is to check out what Estuary has to offer and click the links below.
Build millisecond-latency, scalable, future-proof data pipelines in minutes.
Estuary is the Right-Time Data Platform that integrates all of the systems you use to produce, process, and consume data. Also, providing best-in-class CDC (Change Data Capture).
Estuary unifies today’s batch and streaming paradigms so that your systems, current and future, are synchronized around the same datasets, updating in milliseconds.
We also dig into Databricks vs Snowflake, what matters and what doesn’t, and how to think about modern data architecture without falling for marketing hype.
* On the AI side, we talk about why most LLMs, in the context of developer lifecycles, have changed how we do data, and also about what human skills cannot be replaced.
If you care about leveling up beyond just building pipelines, this one is for you.
Thanks for reading Data Engineering Central! This post is public so feel free to share it.
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe