Data quality monitoring. Data testing. Data observability. Say that five times fast. Are they different words for the same thing? Unique approaches to the same problem? Something else entirely?... Read more »
Imagine you’ve been building houses with a hammer and nails for most of your career, and I gave you a nail gun. But instead of pressing it to the... Read more »
Like bean dip and ogres, layers are the building blocks of the modern data stack. Its powerful selection of tooling components combine to create a single synchronized and extensible... Read more »
I link to Benn Stancil in my posts more than any other data thought leader. I might not always agree with his answers, but I almost always agree with... Read more »
What is data freshness and why is it important? Data freshness, sometimes referred to as data timeliness, is the frequency in which data is updated for consumption. It is... Read more »
Over the past decade, data teams have been simultaneously underwater and riding a wave. We’ve been building modern data stacks, migrating to Snowflake like our lives depended on it,... Read more »
If you don’t like change, data engineering is not for you. Little in this space has escaped reinvention. The most prominent, recent examples are Snowflake and Databricks disrupting the... Read more »
Why do data quality metrics matter? If you’re in data, you’re either currently working on a data quality project or you just wrapped one up. It’s the law of... Read more »
As part of my job, I’m fortunate enough to speak with data leaders far and wide about how they are tackling some of our industry’s biggest challenges, from implementing... Read more »
In his seminal 2017 blog post, The Downfall of the Data Engineer, Maxime Beauchemin wrote that the data engineer had the worst seat at the table. Data technology and... Read more »
