It's the third day of Coalesce 2020 sessions I've been in, and I am so fired up about data testing and what we can do better in my current data team. What's Coalesce 2020? It's a week long conference put on by Fishtown Analytics, the fantastic team behind the data architecture and modeling tool, dbt. So far it's been an immersive and interactive experience using Slack to participate in the data community and in depth discussions on what it means to be part of creating the best data architecture you can create.
So far my favorite sessions have been:
- Getting started with technical blogging - A session full of the basics with some really handy statistics and tips. A motivating session that drove me to write... Well, this!
- Organizational epistemology. Or: How do we know stuff? - A really cool look at how we know what we know... What is a semantic definition on a data set, anyways? What does a day mean when you have multiple time zones where transactions are happening?
- The future of the data warehouse - The uses and misuses of data warehouses, and how and when they are really effective tools in the data pipeline.
- Building a robust data pipeline with dbt, Airflow, and Great Expectations - This was seriously an incredible look at how the Great Expectations testing framework can add some serious power to your data model testing. I need to go play with Great Expectations now...
- Empowering your data team through testing - Really just a session on how important different types of data testing are. Great to see how much the data community values high quality testing!
- Evaluating an offer in the data space - This session was a wonderful look at how to evaluate a company culture while you're interviewing, ask for the right pay, or find out the truth about what it's like to work for a company in the data sphere. I walked away with tools and more confidence to ask for what I know I need from a great data job.
I have a few more Coalesce sessions that I'm going to attend, but this was the bulk of my interest this week. The speakers that Fishtown Analytics chose, the stories that were told about data modeling in real life, and the testing philosophies and data methodologies I learned have left me really excited to build even more tests and better data models in my job. It's been a great opportunity during a tough year to learn so much more about how to be a better data QA engineer.
I'll share more soon about what it's like to work in the data sphere as a quality assurance professional. For now, happy Thursday!