An Analytics Engineer’s Guide to the Galaxy of Microsoft Fabric

Join us for a comprehensive workshop designed to fast-track your journey to becoming a proficient Microsoft Fabric Analytics Engineer. Throughout the day, you will delve into key aspects of Microsoft Fabric, the end-to-end data analytics platform, focusing on essential skills necessary for the DP-600 exam.

During this workshop, you will work through an end-to-end practical solution. You'll start by focusing on data ingestion and transformation using Data Factory, including Pipelines, Dataflows Gen2, and Shortcuts. Afterward, you'll dive into querying and analyzing data using SQL and PySpark. Lastly, you'll learn how to construct a Power BI semantic model utilizing the latest storage mode, Direct Lake. Additionally, you'll explore optimization techniques using third-party tools such as DAX Studio and Tabular Editor.

Throughout the workshop, you will gain insights into Microsoft Fabric capacities, workspaces, security, and more. By the end of the day, you will be equipped with the skills needed to excel as an Analytics Engineer and prepare for success in the DP-600 exam.

Pre-requisites
Basic SQL and/or PySpark
Basic dimensional modeling knowledge
Basic Power BI experience
A laptop with internet connectivity

Introduction to Lakehouse in Microsoft Fabric

Join this session to learn about Lakehouse architecture in Microsoft Fabric. Microsoft Fabric is an end-to-end big data analytics platform that offers many capabilities including data integration, data engineering, data science, data lake, data warehouse, and many more, all in one unified SaaS model.
In this session, you will learn how to create a lakehouse in Microsoft Fabric, load it with sample data using Notebooks/Pipelines, and work with its built-in SQL Endpoint as well as its default Power BI dataset which uses a brand-new storage mode called Direct Lake.

Aggregations in Power BI

Aggregations are one of the most important optimization methods for managing large datasets in Power BI. Combined with Direct Query storage mode, they allow big datasets to be analyzed efficiently by answering high level analytical queries quickly from memory while sending more detailed queries back to the source database.

In this session, you will learn about aggregations, different table storage modes, how to set up aggregation tables and configure them to improve query performance.

Beginner's Guide to Publishing and Sharing Content in Power BI

This introductory session focuses on teaching you the basic concepts of publishing and sharing content in Power BI Service. You will learn about different types of workspaces (My Workspace, Shared Workspace, Premium Workspace, and Premium Per User Workspace) and different user types (Free, Pro, Premium Per User) who can publish content to these workspaces. Next you will learn how you can share your reports either directly out of a workspace or with a workspace App and when to use which method. Finally, you will see examples of sharing in Teams and SharePoint and using subscriptions.

Using Power Apps in Power BI reports: Enabling writeback and more

The Power Apps visual in Power BI is a powerful tool for adding writeback and other functionality from Power Apps applications into Power BI reports.

This session will give you an introduction to Power Apps and how to integrate it with Power BI reports. You will learn how to use the Power Apps visual in a Power BI report to create a Power Apps application that not only can write data back to a SQL data source in Microsoft Fabric, but it can also trigger a Power BI report page refresh so that the changes are immediately reflected in the report. All of this can be done with a few lines of code.

This is an intermediate session.

Hybrid Tables in Power BI

Hybrid tables, a new addition to Power BI, are a game changer in that they provide the best of both worlds by allowing one table to have two storage modes at the same time!

Before the introduction of Hybrid tables, each table in Power BI could only have one storage mode: Import which loaded data entirely into Power BI’s memory or DirectQuery which left data in the back-end data source. When working with near real time requirements, Power BI developers had to use tables with DirectQuery; however, DirectQuery by itself could result in less than optimum query performance for large datasets and put a burden on the backend system.
Hybrid tables combine the two storage modes and therefore provide improved query performance for historical data by loading them into memory (Import) and provide near real time capability for recent data (DirectQuery).

Join this session to learn about different table storage modes in Power BI including the newest Hybrid option and how to use it.

Hybrid Tables are planned to be in public preview in December 2021.

Incremental Refresh and Hybrid Tables in Power BI

Incremental Refresh makes it possible for Power BI to handle large datasets by partitioning the data into segments and making refreshes faster, more reliable, and less resource intensive.
Hybrid tables, a new addition, can be easily configured for a table with Incremental Refresh to enable different storage modes for its different partitions. This allows historical data to load into Power BI’s memory (import) for super-fast query performance and to leave the most recent data in the backend (DirectQuery) for near real time results.

Join this session to learn about different table storage modes in Power BI including Hybrid, understand the benefits of Incremental Refresh, and see how easy it is to set up Hybrid Tables along with Incremental Refresh to provide super-fast performance for historical data while having recent data be available to users in near real time.

Aggregations in Power BI: Automatic and User Defined

Aggregations are one of the most important optimization methods for managing large datasets in Power BI. Combined with Direct Query storage mode, they allow big datasets to be analyzed efficiently by answering high level analytical queries from memory while sending more detailed queries to the back-end source systems.

There are two options for aggregations: User-Defined Aggregations and Automatic Aggregations. User-Defined Aggregations are set up from Power BI Desktop and are designed/maintained by developers. Automatic Aggregations are enabled from Power BI service and are designed/maintained by Power BI Service using machine learning to design an optimum set of aggregations. Both types of aggregations can be used in a Power BI dataset.

In this session, you will learn about aggregations, different table storage modes, how to set up and configure user defined aggregations as well as automatic aggregations, and how to monitor their use.

Reporting with Azure Synapse Analytics and Power BI

This session introduces you to Azure Synapse Analytics with a focus on Azure Synapse Serverless SQL Pools and teaches you best practices for building Power BI models from Azure Synapse Analytics for cost effective data lake reporting.

This session assumed the attendee is familiar with basic modeling in Power BI. This session does not include provisioning steps for an Azure Synapse Analytics workspace.

A beginner's guide to PowerApps and PowerBI

This is an introductory session to Power Apps and Power BI for absolute beginners. Join this session to learn the fundamental concepts and see how easy it is to use the low-code/no-code features of Power Apps and Power BI to build fully functional transactional/reporting systems with a few clicks. During the demo you will learn how to build a simple inventory application using Power Apps with Excel as the data source and Power BI as the dashboard layer.

No previous experience required.

Power BI Direct Lake storage mode: How to achieve blazing fast performance without importing data

Power BI engine in Microsoft Fabric has been significantly revamped to work directly with Delta files in OneLake. This brand-new storage mode is called Direct Lake which allows Power BI to achieve super-fast query performance on billion row datasets without having to import the data into Power BI. Join this session to learn how you can work with Direct Lake with just a few clicks.

Getting started with the Warehouse experience in Microsoft Fabric

Join this session to learn about the Warehouse experience within Microsoft Fabric. Microsoft Fabric is an end-to-end big data Software as a Service (SaaS) analytical platform that offers many capabilities. One of those capabilities is the Warehouse experience which provides a fully cloud native, massively parallel processing, self-optimizing, and scalable SQL engine with ACID transactional guarantee. The Warehouse option is a great choice for teams who prefer to use SQL as their main development language.

In this session, you will learn how to create a warehouse in Microsoft Fabric, load it with sample data using Data Factory pipelines and Dataflows Gen 2, write ad-hoc queries using SQL, and work with the default and custom Power BI datasets to empower end user analytics.

Introduction to Microsoft Fabric

Microsoft Fabric is a comprehensive big data analytics platform that seamlessly integrates data integration, data engineering, data science, analytics, and more into a unified Software as a Service model with a streamlined capacity pricing model.

Join this demo-intensive session for an insightful introduction to Microsoft Fabric.

Enabling writeback in Power BI reports using PowerApps

Discover the dynamic duo of Power Apps and Power BI in this session, where we delve into the seamless integration of Power Apps visuals within Power BI reports with a focus on writeback to SQL data sources including those in Microsoft Fabric.
Learn how to effortlessly empower your reports with real-time data interaction and writeback capabilities. Learn the art of triggering automatic page refreshes in Power BI, ensuring immediate reflection of data changes.
Leave equipped with the knowledge to achieve these accomplishments with ease, armed with just a few lines of code.

This is an intermediate session.