Why DBT will one day be bigger than Spark
The world of data is moving and shaking again. Ever since Hadoop came around, people were offloading workloads from their data warehouses to the new and shiny data lakes. And it didn’t take long before Spark, which was open sourced in 2010, became the standard processing engine on data lakes.
Now we see a reverse trend, back to the data warehouse. And with that trend, DBT has risen as almost the de-facto standard for doing transformations on modern cloud-native data warehouses. Using DBT, people are discovering that they can build their data pipelines faster, with fewer engineers and with less maintenance.
Credit: Kris Peeters
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