Data will power every piece of our existence in the near future. I collect “Data Points” to help understand & shape this future.

If you want to support this, please share it on Twitter, LinkedIn, or Facebook.

Einar Orr, Co-founder of lakeFS and long-time data hero makes a good case for using meaningful metrics to evaluate data teams. The three metrics she suggests are:

She makes a good case for the three and gives some insights on how to treat each of these metrics. I like that approach and find it feasible as well, but I still like to keep to the “good old stuff” which simply assumes data teams are good development teams, and as such we should really focus on the four key metrics first:

If you take a look at this, it means I rather focus on the foundations, teams that are quick to fix mistakes, and as a result have high data quality. Teams that are quick to react to changes in the environment and deploy frequently and as a result have a high development velocity and so on… But I’m sure both approaches work well.

I’ve been playing around with dagster lately, comparing it to prefect and airflow and I came to like it. Two reasons make dagster so much fun. Of the currently in vogue data orchestrators it’s the one that:

What’s the vision? To orchestrate, basically build an overarching “DAG’’ regardless of your tool choice. You can use a Jupyter notebook, Spark, SQL, whatever, dagster doesn’t care. That resonates very well with what is currently happening in the typical data team and will very likely continue to happen in most teams.

Why is it fun to develop? First and foremost, because dagster makes it easy to write tests! Tests for the smallest units, tests for the whole flow. You can mock data and run things on your laptop quickly, you can easily swap environments and run against either integration or a production environment. That’s made possible by outputs & inputs and a stronger system around the “metadata of the flow”.

As a resource, for now, I recommend simply take a look at the journey of Mapbox, until I get around to write a “test-driven dagster” tutorial.

I sometimes feel like the unofficial Airbyte evangelist… Robert Stolz of preset wrote a comparison of the two major upcoming open-source data integration solutions Airbyte and Meltano. He uses it for a smaller project so the comparison does not involve the question of scale. But he does give a good introduction to both tools.

If you’re on the hunt for a new data integration tool, go read the article.

Thanks for reading this far! I’d also love it if you shared this newsletter with people whom you think might be interested in it.

I did get some good writing in this week, and was able to produce a piece I really like: