Over the past five years, there's been a paradigm shift in data engineering. Here's how data observability fits into a “modern” data stack today.

How does Stripe ensure end-to-end data reliability? Here, we walk through some real-life lessons learned and best practices for doing just that.

What are the intricacies of ETL and ELT? Here are their key characteristics, advantages and drawbacks, plus some use cases where you'd use each.

Here's a brief history of one of the powerhouse organizations bringing change and innovation to the data space: Snowflake.

What is data vault modeling? From common use cases to best practices to framework architecture, this post has you covered. Read on.

How do you break the cycle of confusing, unusable data and use your vast data stores to drive business value? "Data discovery" is key.

How do modern data teams build and enforce data reliability? We researched 20 use cases from real-life data teams in order to find out.

Welcome to your quick primer on all things data ingestion. From components and patterns to the best practices for implementation, read on.

Databricks has been a key innovator in data over the past decade. Here's a rundown of their history and impact on data engineering and ML.