Christian Henrik Reich

Microsoft Fabric Lakehouses receive a great deal of attention, but Fabric Warehouse is often underused or misunderstood in lakehouse architecture. As the platform has evolved, the Warehouse has become a more important part of the broader Fabric story, especially for teams that need strong...

An Apache Spark Query's Journey Through the Layers of Microsoft Fabric Join us for an exciting deep dive into the heart of Apache Spark! We'll take you on a journey to see exactly how your Spark queries get executed, both within Apache Spark itself and through the different layers of Microsoft Fabric.

SQL Server skills transfer to Microsoft Fabric. In a one-day workshop covering Lakehouses and Warehouses, you’ll dive into engines, query plans, stats, and Delta Lake, and leave confident applying what you already know.

Follow an Apache Spark SQL query from optimizer to execution across Spark and Microsoft Fabric. Catalyst, Tungsten, Fabric’s native C++ engine, and the Delta Lake/Parquet storage layers are covered, grounded in Spark source code, with practical tips to optimize for speed and cost.

A practical session on going beyond chatbots: using modern AI architectures and services to turn unstructured data (images, audio, text) into usable insights and integrate it with relational data, with actionable next steps to get started.

When Microsoft Fabric was released, it introduced Apache Spark as its default engine for general data processing. While Spark is powerful for big data scenarios, it’s not always the best solution for many small and medium-sized workloads, due to both costs and performance loss from administrative overhead.

Microsoft Fabric Lakehouses get lots of attention, yet the Warehouse’s evolution and true potential in a lakehouse solution are often overlooked.This session covers:* Engine deep dive* Performance* Warehouse’s role in lakehouse solutions* Interoperability with Spark and lakehousesAttendees will see how the Warehouse has evolved and how to apply it effectively in a lakehouse solution.

When Microsoft Fabric was released, it introduced Apache Spark as its default engine for general data processing. While Spark is powerful for big data scenarios, it’s not always the best solution for many small and medium-sized workloads, due to both costs and performance loss from administrative overhead.

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.

Microsoft Fabric is becoming the one-stop shop for data in Azure, including machine learning and AI. Fabric’s maturity is starting to enable real projects with its machine learning and AI capabilities.

While Microsoft Fabric initially focused on Spark, its distributed approach can be overkill for smaller tasks. Python Notebooks often deliver more efficient, cost-effective performance. This session shows when and why Python outperforms Spark.

Headline: An Apache Spark Query's Journey Through the Layers of Microsoft Fabric Abstract: Join us for an exciting deep dive into the heart of Apache Spark! We'll take you on a journey to see exactly how your Spark queries get executed, both within Apache Spark itself and through the different layers of Microsoft Fabric.

At some point, when working with data, the star schema pops up. There is a lot of misconception about the star schema, but realising it is designed for our data technologies and our data technologies is optimised for it, it becomes a very powerfull pattern.