Implementing GraphQL with Microsoft Fabric

Many developers are looking to use GraphQL in their apps but they face resistance as database engineers are uncomfortable with using GraphQL as they do not know how to work with it. Microsoft Fabric provides an simple way to implement GraphQL with any of the different Fabric data stores, including lakehouses, warehouses, and SQL DB.

In this session, attendees will learn how they can take data inside of Microsoft Fabric, and implement GraphQL for developers and still retain the features and functionality of a queried data store. This means one data store will be readily used for development and analytics using the different formats each user group finds best suits them.

Why architecting a Microsoft Fabric Environment may include a Fabric SQL DB

Businesses today want well designed databases which can be used not only by applications but are part of an analytical solution which allows people to interact visually with the data in reports. Are you interested in decreasing and centralizing your Azure costs while continuing to support applications which need a SQL database? Are you interested in migrating an on-premises SQL Server database to the cloud or move away from decentralized manage instance databases? Fabric SQL DB can be the solution as it provides the ability to combine SQL Server functionality with Business Intelligence Analysis.
For many existing applications, having a SQL Database is a requirement. We will review the processes for doing maintenance for SQL DB inside of Microsoft Fabric, focusing on the “knob free” methods to minimize the steps involved and migration from an on prem database. We will examine the different cost models to demonstrate how this solution can be used to minimize costs and decrease management effort through automated maintenance and centralized resource monitoring.
This session will provide you with the use cases to determine when and why an SQL DB inside of Fabric can benefit your data environment.

How do I figure out if I should use a Lakehouse or a Data Warehouse

With Microsoft Fabric there are two distinct data structures which are very similar: the Lakehouse and the Warehouse. Despite their similarities in operational costs, SQL Endpoint access, usage of parquet files, data masking, TSQL querying, and other features typically associated with SQL Server, it raises the question: why choose one over the other? This session aims to explore the architectural considerations that guide the optimal selection for your organization. We will also review the structure showing why the performance is so much faster than Synapse Serverless.
This session will be crucial for those tasked with making informed data architecture decisions for their organizations as you will get the answer to your data questions.

Developing with Spark for Microsoft Fabric with Copilot

Developing solutions in Microsoft Fabric can include Apache Spark and in this environment that includes Copilot generated code to document, comment, debug and create error handling. We will show you how to incorporate Copilot to perform these task either within Fabric or in a tool you may be more comfortable using VS Code.

We will also talk about the unique methods that Fabric includes for improving developer productivity by utilizing drag and drop methods for creating common code patterns. We will also demonstrate how to use Data Wrangler to analyze and clean your code and add the spark dataframe modifications to your code.

We will review how to use this code in a data pipeline and monitor results. If you want to improve your Spark productivity in Fabric, you won’t want to miss this session.

Data Wrangling: Code Generation in Fabric without Copilot

If you were hoping to get some assistance with writing spark code and do not have an F64 SKU, this session is for you. We will review how to examine and modify your lakehouse code using Python even if you don't know how to write it using Data Wrangler. You won't want to miss this session to help you modify your data and include it as part of your ETL Pipeline.

Developing a AI solutions with your data

In this hands-on session you will learn how to provide answers using your data as the source by incorporating different AI elements. We will take a look at different methods for providing these answers including a knowledge management agent, chatbots, analytics agents or generative AI models to answer prompts about your data. We will review how each of these different elements work and how and why to generate them and select the appropriate tool for different data needs.
We will explore the different Azure components needed to create these systems and how you will need to structure your data to consume it. We will review the different features of Azure AI and how they can be combined to provide the required solutions.
During this session, we will explore the different generative AI models to determine which one best meets the needs of your organization. You will learn how to create a chatbot, and how it can be used in coordination with other models to create a solution.
Be sure to bring your laptop to see how you can create AI solutions during this session. We will walk through the code samples and Azure elements required to create these solutions. You will walk away from this session with the knowledge you need to implement an AI tool for your organization.

AI in Power BI

AI is changing the game in a big way, making things quicker and boosting the quality of results across various applications, and Power BI is no exception. In our upcoming session, we're diving into how Copilot can lend us a hand in writing DAX, designing visuals, and unlocking other cool features that make reports more accessible and cut down on the time it takes to whip up some awesome reporting solutions in Power BI. We're also going to explore how Copilot shines in the semantic model and the Fabric service, helping you churn out solutions faster that are a breeze for others to use and understand. You will want to learn how to develop complex elements simpler and friendlier than ever before.

Managing Power BI Development with CI/CD

This year has brought a number of changes to Power BI which provide new ways to create a continuous integration and continuous delivery (CI/CD) environment. During the session new tools and features are reviewed to help you create more effective DevOps Management in Power BI while demonstrating examples of how to incorporate source control. Organizing and managing workspaces is also an important part of the release lifecycle, which we will demonstrate as this can impact how releases are managed via pipelines. To better understand the options available within your organization, all of the licensing requirements for source control will be reviewed to understand what features are available in different versions.

Lakehouse or Warehouse : Which is the right choice in Microsoft Fabric

Microsoft Fabric introduced the Lakehouse and the Warehouse as two different data structures, but how different are they really? Both cost the same amount to run, both allow access via a SQL Endpoint, use parquet files, include data masking, TSQL querying and other features commonly associated with SQL Server. Why do you want to use one over another? In this session we will look at the architecture decisions which determine which is the best choice in your organization. We will also demonstrate how you can convert a dedicated pool from Azure Synapse DW into a lakehouse. If you want to consolidate your data from a Synapse solution to Microsoft Fabric you will find this session very helpful. We will provide you with reasons for why you might want to consolidate your data and when this architecture might not be the best choice for your environment. If you are looking to design a data architecture for your organization this session will help you make the right decisions going forward.

Implementing Self-Service reporting in Fabric

Creating an environment where users are able to create reports without having to worry about data modeling, how to create security or develop complex measures is a goal for many organizations. Setting up an environment which will make people be able to create reports while following the design settings of the organization will make for a more impactful Fabric implementation.

In this session we will review advanced theme development, structure and organization of workspaces, and other governance practices that will provide the required support needed for users to better be able to create and distribute reports.

Implementing a Self-Service Reporting Solution

Providing an environment where business users are able to develop their own reports is a goal of many companies. Providing this environment in Power BI takes more than just pointing users to a data model, as the data environment needs to provide an environment where users can utilize design standards, understand which measures to use so that they will be able to do their own analysis. We will look at all of the steps needed to create detailed themes, templates, and supportive model designs. We will also review what is required to use new tools such as Copilot for Power BI, Microsoft's implementation of ChatGPT and how it can be incorporated to insure people have the ability to generate the reports they want even if they are not Power BI fluent. Learn everything you need to include to make non-Power BI experts look good and create meaningful analysis.

Copiloting Fabric

In this session we will look at a number of different things that you can have Copilot do for you with Microsoft Fabric, including one that does not require an F64 Fabric License! We will review how Copilot can help you document and improve your code from DAX to Spark. You will see what kind of reports and ELT solutions that using AI with Copilot can create and how using it can improve productivity.

Creating an Organizational Data Store with Microsoft Fabric

If you were wondering what Microsoft Fabric is and how it can be used to organize and report on data, you will want to attend this session. Microsoft introduced Fabric as a new product in 2023, which incorporated a number of features from Power BI, Synapse, Azure Data Factory, Purview and Azure ML, and added new features as well. In this session I will describe what business needs Fabric was designed to solve and how to incorporate these changes into your environment. If you are currently using Power BI and wonder how these changes will impact your business, you will want to attend as you will learn how the changes impact Power BI. In the accompanying demonstrations you will see how you can use Microsoft Fabric to organize your data in OneLake, develop virtual databases, and connect the data to create reports in Power BI. We will also review the incorporation of ChatGPT, which Microsoft calls CoPilot, to generates reports for you, which you can release right after you create them or choose to incorporate those as a starting point to modify or create new Power BI reports.

Implementing Data Science in Microsoft Fabric

Developing data science solutions in Microsoft Fabric uses a number of components to create machine learning models and easily incorporate data from OneLake into dataframes. This session will also review some time saving tools added to Fabric including Data Wrangler and the Fabric Runtime. We will take a look at how you can incorporate these tools with notebooks to create a machine learning model and implement it in Microsoft Fabric data pipelines. In this session we will walk through the data process starting with data exploration, developing a dataset, experimenting and evaluating algorithms, modeling and implementing the solution.

Direct Lake and Direct Query In Fabric

Direct Query has been the choice for situations when businesses wanted large data models and where data entered in a database was immediately available. Direct Lake provides a new method for achieving the goals previously only available in Direct Query. Direct Lake was introduced to Power BI this year, and it can also be used to analyze large data volumes without needing to import the data, while providing good performance to users at scale who want to access the data. In this session we will see how you can use large data volumes in Power BI and take a look at how well it performs and scales using Direct Lake. We will examine how One Lake, lakehouses, and SQL Endpoints can be used together to provide optimal performance with Direct Lake. To see if this solution will work in your data environment, you will receive an understanding of the licensing required to implement this solution so that you can ensure that you have the knowledge you need.

Implementing Source control in Fabric

Creating a continuous integration and continuous delivery (CI/CD) in Power BI is possible now with PBIP files. In this session we will review how to implement it. We will walk through examples to provide attendees the information they need to add Power BI to CI/CD process used for other coding environments. See how you can automate assigning workspaces, adding users and managing deployments, and creating and editing pipelines.

Data Ingestion in Fabric

In this session we will review 3 different methods for ingesting data, using the copy command, using Fabric's Dataflow2 and traditional Dataflows and discuss the reasons for using each method. The copy command has been updated and Dataflow2 is part of Fabric. We will review how these compare to traditional Synapse Ingestion dataflows and when it makes sense to use each.

Building an Fabric Environment for Power BI

Microsoft Fabric contains a number of different tools which you can use to create a data lake house environment perfect for Power BI. Learn the steps that are involved to develop and monitor data transformation pipelines, create a data lake house environment, and produce a final data store. Depending on the different data elements within the organization, the final data store could be a lakehouse, SQL Endpoint, data warehouse or Power BI model. We will review the different use cases so that users will understand which combination is best given properties of data and skillsets in your environment.

Implementing Data Analytics with Microsoft Fabric

Microsoft Fabric has incorporated a number of different elements into one environment for data lakes with OneLake, data warehousing, machine learning, data transformation, and reporting. In this session we will look at using OneLake for data lakehouses and demonstrating how OneLake can be used instead of a database highlighting the performance improvements which have been made. Demonstrations will show the ability to use Fabric to connect to data from different sources and from different data lakes to ensure compliance with GDPR or other location-based regulations with OneLake. We will examine what a Fabric data lakehouse entails and how the data is integrated into Power BI for reporting. Changes in Spark clusters and Delta file implementation are examined to ensure you will understand how these improvements will impact your data movement pipelines and machine learning tasks. Data science workflows are reviewed to provide a good explanation of not only how to use them, but also the best practices for integrating these objects into pipelines. We will investigate how using the new Dataflow2 data transformations can speed up development and when they would be a good implementation choice. We will also examine the different ways Copilot is incorporated within Fabric so that your organization can be on the cutting edge of AI development with ChatGPT technology. This session will show you how to leverage Fabric’s different components for data driven decision making within your organization.

Data Science in Fabric

In this session we are going to look at creating a machine learning model and optimizing algorithm selection with Microsoft Fabric. After reviewing how that works, attendees will learn how they can either incorporate a low code solution to pick the best algorithm for the task. We will be examining the impact of Fabric Spark pools to those who may be familiar with Synapse or Databricks and how these changes improve the Spark development process. We will take a look at everything from creating to implementation incorporating new Fabric functionality.

Architecting a data solution in Fabric

With the introduction of Microsoft Fabric, the methods used for creating a data lake and using it in Power BI have changed. In this session we will take a look at how you can use the different components of Fabric to Architect a solution using the features of One lake with a Fabric workload. The session focuses on the differences with Fabric development to the methods you may have created a solution using Synapse and Power BI.

Developing a Self-Service Power BI report development environment

Power BI is designed to be a reporting tool for the masses as report visualizations can be created with a few clicks. While it looks easy enough there several elements including the data model and DAX Measures which need to be created to make easy report creation possible. In this session we are going to explore the practices and methods which will make it possible for non-data professionals to develop their own reports in Power BI using models and templates created for that purpose. Attendees will learn what needs to be built to create a Power BI environment designed for end user report creation. Learning the elements which need to be included in the data model and what needs to be included in the report templates will provide a foundation for self-service development.

Data Model Design and Optimization for Fabric

If you have designed a sematic model for a data warehouse, you know most of what you need to develop a semantic model for Power BI, but there are some important differences to improve the overall performance and maintenance within Fabric. In this session we will review the differences and include design patterns for Row Level Security, data aggregations, and DAX Development. We will also review different design patterns for Composite Models, Direct Query, Import and Dual modes and how to organize them for optimal Fabric environments.

Power BI DAX in a Day

If you have started working with DAX and would like to take your code to the next level, this session is for you. In this all day session, you will learn not only how to better write and understand DAX, but to use tools which can assist to write DAX faster and create better performing measures. This session will provide the ability to learn not only How but Why to use different DAX elements to be able to quickly develop complex measures.
The course will cover the following Topics
• Evaluation Contexts
• Improving DAX with Third-Party Tools and Best Practices
• Variable Uses and tricks
• Iterators
• Deep Dive into CALCULATE
• Advanced Time Intelligence
• Improving DAX Performance
Demonstrations and follow along exercises will provide the opportunity for you to not only hear about the concepts but see how they are implemented in code you can refer to later.

Data Lake Mangement with Azure Synapse and Delta Lake

Data lake management is required to ensure that the information stored can be readily analyzed. Spark Delta Lake moves the structure of a data lake closer to a database and is definitely something you are going to want to implement in Azure Synapse Analytics. In this session you will learn how to apply Spark Delta Lake to improve data quality, query speed, and review backups of files stored in the data lake. Implementing these strategies can improve analysis capabilities as data can be analyzed more like a data warehouse, without all of the transformation and storage costs. Using Delta Lake, attendees will see how to ensure the format is known when it was added, rather than finding out years later that no one is able to determine what is in the files. As data lakes can be queried like a database, we will examine how to speed analysis with Delta Lake by indexing flat files and consolidating the data to improve query performance. Need to look at what the data looked like prior to a change being made to the data in the lake? We will look at ways to travel back in time to review the data. This session will provide the skills needed to improve your data lake management with Delta Lake making it even easier to analyze data in Azure Synapse.

Diving into Dax: 7 things that will help you write better DAX and understand why

If you use Power BI you have written DAX. DAX can be complicated and understanding better how the DAX Engine works can help you write better performing DAX. There are a couple of third party tools which can help, so we will review Bravo, a relatively new free tool you may not know about and DAX Studio. We will use those tools when we review DAX Formatting with Bravo, context transition, different filtering methods and why you would use them, Calculated tables, USERELATIONSHIP() tricks, when to use VALUES, and validating performance with DaxStudio. After this session you will be able to incorporate some useful tools, write better performing DAX, measure the performance and better understand the DAX Engine in Power BI.

Implementing a Self-Service Power BI Solution

Providing an environment where business users are able to develop their own reports is a goal of many companies. Providing this environment in Power BI takes more than just pointing users to a data model, as the data environment needs to provide an environment where users can utilize design standards, understand which measures to use so that they will be able to do their own analysis. We will look at all of the steps needed to create detailed themes, templates, and supportive model designs. Learn everything you need to include to make non-Power BI experts look good and create meaningful analysis.

Data storage and Usage in Microsoft Fabric

Microsoft Fabric has different storage and exploration for data which were not available in Synapse or Power BI. One Lake, Data Lakehouse, and SQL Endpoints are three different ways of organizing data that may provide a significant benefit to your environment. In this session we will explore these different storage options and the use cases for each. We will also review a new method for exploring data stored in One Lake, Data Wrangler. The demos will provide examples and since Microsoft Fabric is still in preview, you will be able to work through them later yourself.

Data Engineering in Microsoft Fabric

Microsoft Fabric includes a lot of different elements, including Data Engineering. Data Engineering includes notebooks, pipelines, lakehouses and data pipelines which you will have a better understanding of how they work together within fabric. In the demos for this session you will see what functionality these elements provide and how you can use them in your data solutions.

Introduction to Microsoft Fabric

Microsoft Fabric was introduced in May of 2023 and contains elements of Power BI, Synapse, and Machine Learning. In this session we will review the different components and focus on what elements can be used to architect a data solution, how fabric differs from other previous technologies and how it doesn't and why you would want to use it in your environment.

Developing AI solutions with your data

In this hands-on session you will learn how to provide answers using your data as the source by incorporating different AI elements. We will take a look at different methods for providing these answers including a knowledge management agent, chatbots, analytics agents or generative AI models to answer prompts about your data. We will review how each of these different elements work and how and why to generate them and select the appropriate tool for different data needs.
We will explore the different Azure components needed to create these systems and how you will need to structure your data to consume it. We will review the different features of Azure AI and how they can be combined to provide the required solutions.
During this session, we will explore the different generative AI models to determine which one best meets the needs of your organization. You will learn how to create a chatbot, and how it can be used in coordination with other models to create a solution.
Be sure to bring your laptop to see how you can create AI solutions during this session. We will walk through the code samples and Azure elements required to create these solutions. You will walk away from this session with the knowledge you need to implement an AI tool for your organization.

Learning Goals
1. Learn to Utilize AI for Data-Driven Answers: Participants will be educated on how to leverage their own data using various AI elements such as knowledge management agents, chatbots, analytics agents, and generative AI models. The focus will be on different methods to extract answers from data, understanding which AI tool suits a particular data need.

2. Explore Azure AI Components and Data Structuring: The session aims to provide insight into the Azure ecosystem, particularly the AI components available and how they can be orchestrated to deliver solutions. Attendees will learn how to structure their data effectively for consumption by these AI models, ensuring efficient and practical implementation.

3. Hands-On Experience in AI Solution Creation: Attendees are encouraged to bring their laptops for a practical experience, where they will be guided through code samples and the usage of Azure elements necessary for creating AI solutions. The goal is to equip participants with the practical skills and knowledge to develop and implement AI tools within their organizations.

Fabric Data Security in Lakehouses and Data Warehouse

When data moves from a database to a lakehouse or a warehouse, how to you secure the data? How do you provide access to it? Can you implement row level security on the the lakehouse? Can you mask the data and have it the data appear as masked inside of a Semantic Model used for Power BI reporting? In this session, you will learn how you can implement security on lakehouses and data warehouses inside of Fabric and which one you should implement with your data. You will learn how to restrict access to data and how to implement object and row level security at the database level. See how you can implement similar user security to what you may have deployed in SQL Server inside of Fabric Endpoints in SQL Server Managment Studio