Netflix is the streaming service we all know and love. They deliver delivers over six petabytes of data to customers daily. This post covers some of Laura Pruitt's insights into how Netflix maintains the quality of their core dataset.

The most effective data engineering teams are those who successfully shift from a toil culture— a series of manual one-off tasks, firefights, and moments of heroism—to a reliability culture with planned, repeatable, and scalable processes.

In 2022, the data space matured by leaps and bounds. What's in store for 2023? Here are nine top data trends we think we'll see in the coming year.

In the "Data in Practice" series, we talk to real data engineers who have put data reliability engineering concepts into practice, learning from their challenges and successes in the real world.

Business runs better when its backed by data observability. But in the interest of "show, don't tell," let's point out some fairly recent examples.

At the heart of every digital transformation effort, organizations are betting on new digital technologies to create an organizational revolution. Without data observability, this revolution is more of a pipe dream than a reality for most organizations.

In the "Data in Practice" series, we talk to real data engineers who have put data reliability engineering concepts into practice, learning from their challenges and successes in the real world.

Working on data quality used to mean simply "fixing the issues." Today, the understanding is more nuanced. Data quality techniques don't actually fix anything. Why not? Read on.

Think data observability isn't for you? Maybe you're right. But make sure you aren't operating under some of these common misconceptions.