Apache Spark Declarative Pipelines: Simplifying Data Workflows with SQL and Python

A New Era for Data Engineering: Declarative Pipelines Land in Apache Spark

For years, data engineers relied on writing out each step of their ETL (Extract, Transform, Load) pipelines in painstaking detail. Think lots of custom code just to juggle dependencies, wrangle changes in data sources, and ensure timely delivery of insights. But now, Apache Spark is flipping the script with the introduction of Declarative Pipelines.

The premise is refreshingly simple: instead of building out the “how”—every loop, every dependency—engineers can simply declare what they want the pipeline to do. Spark’s engine takes care of interpreting those instructions and figuring out the optimal execution plan under the hood. Whether you’re using Python or SQL, this means you spend less time on orchestration and more time focused on the data and outcomes that matter.

The impact on development speed is dramatic. According to Databricks—the original creators behind Spark—this approach can shrink pipeline build times by up to 90%. That’s not just about getting to production faster. Declarative components are modular and reusable, making it easier to maintain quality standards, handle schema changes as sources evolve, and keep everything running smoothly. Less manual patchwork means a more reliable, future-proof data stack.

And there’s more: this new framework isn’t locked away behind enterprise paywalls. Databricks is donating these capabilities to the open source community. That move doesn’t just broaden who gets to use and experiment with declarative ETL, it paves the way for deeper collaboration and innovation across companies and teams around the world. No more vendor lock-in.

For modern data teams, these advances promise more than faster pipelines. They mean less technical debt, unified batch and streaming workflows, and robust safeguards against breakage as data landscapes shift. By raising the level of abstraction, Apache Spark’s Declarative Pipelines help make data engineering accessible to more people, reduce maintenance headaches, and—ultimately—enable organizations to adapt and scale with confidence.

If you want a deeper dive, head over to VentureBeat’s article here: Databricks open sources declarative ETL framework powering 90% faster pipeline builds.