Data Engineering vs Data Science

I was in a local technology Slack and saw the question:

Tell me, what is data engineering and how is it different than data science? What [Python] tools do you use?

It's a good question and it's worth considering. Here's what I think:

Ted Danson reading a slip of paper. Caption: "Okay, here we go"

Data engineers are concerned with how data:

• Lands in a system
• Moves through a system
• Interacts with business processes and application logic
• Is stored
• Is governed

Definitions differ, but I think that's a good starting place.

There is a huge and diverse ecosystem of tools out there. I would highlight the following as tools with strong Python tooling:

• Kafka (for pub/sub)
• Spark for data processing (including the Spark Streaming API for stream processing)
• Airflow for workflow management
• Pandas for data "wrangling"

And you can find tons of tools on the awesome list.

Lastly, how do data scientists differ from data engineers? I would argue that they're both roles that are concerned with connecting data to business processes. They differ the same way their names differ:

• A data engineer is concerned with designing and building systems that make data available and actionable in a cost-effective manner.
• A data scientist performs experiments, the results of which are (sometimes) actionable insights or automation.

I hope that explanation helps you. If you see something I didn't explain very well, or got wrong, please chime in with that 👇