DevConf.CZ 2020

Until very recently, Apache Spark was the de facto choice of framework for batch data processing at scale. For Python (or new) developers, diving into Spark is challenging, as it requires learning the Java infrastructure, memory and configuration management. The multiple layers of indirection also make it harder to debug errors, especially when dealing with the PySpark API.

With Dask, a pure Python framework for parallel computing, Python developers have now an intuitive and elaborate way of building scalable data pipelines. In this talk, we'll be using a data aggregation use-case to highlight the important differences between the two frameworks, and make it clear the overall benefits of moving from one framework to other.

By the end of the talk, developers/ data engineers/ scientists, would have a framework and benchmarks to refer to, to make an informed decision while building their production Data Engineering pipelines.