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Outperform Spark with Python Notebooks in Fabric

Session: When Microsoft Fabric was released, it came with Apache Spark out of the box. Spark’s ability to work with more programming languages opened up possibilities for creating data-driven and automated lakehouses. On the other hand, Spark’s primary feature to scale out and handle large amounts of data will, in many cases, be over-dimensioned, less performant, and more costly when working with trivial workloads. With Python Notebooks, we have a better tool for handling metadata, automation, and processing of more trivial workloads, while still having the option to use Spark Notebooks for handling more demanding processing.

We will cover: * The difference between Python Notebooks and a Single Node Spark cluster, and why Spark Notebooks are more costly and less performant with certain types of workloads. * When to use Python Notebooks and when to use Spark Notebooks. * Where to use Python Notebooks in a meta-driven Lakehouse * A brief introduction to tooling and move workload between Python Notebooks and Spark Notebooks. * How to avoid overload the Lakehouse tech stack with python technologies. * Costs

Speakers: Christian Henrik Reich \| Principal Architect @ twoday Data & AI

Agenda (CEST, UTC+2):

18u30 Welcome and introductions

18u30 Outperform Spark with Python Notebooks in Fabric (60 minutes)

19u45 Session End