Outperform Spark with Python Notebooks in Fabric

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

After this session, attendees will have an understanding of how to apply Python Notebooks, as well as Spark Notebooks, to get the most out of a Fabric Capacity for data processing.

ML and AI Capabilities in Microsoft Fabric

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. As with many other aspects of Fabric, there are also new libraries and tools for machine learning and AI. These might be different, especially for those coming from Azure ML.

The session will cover:

* Basic and AutoML machine learning
* Hyperparameter tuning
* OpenAI/GenAI
* MLOps, including model tracking, model repository, and model serving
* How is AzureML still relevant?
* Fabric Workspace layout, capacities, and costs

Attendees will leave with practical insights into using AutoML, hyperparameter tuning, and MLOps within Fabric, along with how OpenAI and GenAI fit into the ecosystem. We’ll also discuss how Azure ML remains relevant and how to navigate Fabric’s workspace, capacities, and associated costs to maximize your project's efficiency and potential.

Setting up Fabric Capacities in Azure: How to and considerations

Fabric has 2 different licensing models, Power BI and Fabric Capacities. In this session, we will walk through Fabric Capacities. We will cover:

* When to select Fabric Capacity over Power BI licensing
* Azure requirements and roles
* Fabric Roles
* Location considerations
* Pricing considerations
* How to enable in Fabric

Modelling and indexing your dataware/lakehouse house

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. This session is deep technical and about designing star schemas and indexing them correctly, and how it roots in our technologies. The end result is that attendee can build a data warehouse for less money, and having a self-service platform like Power BI holding more data compared to using other patterns.

What is a DTU? And how to right-size an Azure SQL Database

When working in Azure, we more often that not has to provision an Azure SQL Database and we have to decide between a myriad of options which both affect performance and costs. This session will demystify the DTU, and give some tools to select between the DTU and vCore purchasing model. After the session the attendee should provision the right Azure SQL Database to the right costs.

The attendee should have DBA knowledge about SQL Server in generel.
We are focusing on Azure SQL Database, so Azure knowledge is plus but now needed.

An Apache Spark query's journey through the layers of Databricks

A deep-dive session about Spark internals, where we explore how queries are executed in Apache Spark and within the layers of Databricks.

We will cover:

* Spark SQL and Catalyst
* A note on Tungsten
* Delta Lake
* Parquet files

These insights will be supported by glimpses into the official Apache Spark source code on GitHub.

The takeaway should be a better understanding of how queries are executed and some tools for problem-solving and optimizing for speed or cost.

An Apache Spark query's journey through the layers of Microsoft Fabric

A deep-dive session about Spark internals, where we explore how queries are executed in Apache Spark and within the layers of Microsoft Fabric.

We will cover:

* Spark SQL and Catalyst
* A note on Tungsten
* Delta Lake
* Parquet files

These insights will be supported by glimpses into the official Apache Spark source code on GitHub.

The takeaway should be a better understanding of how queries are executed and some tools for problem-solving and optimizing for speed or cost.