Building a Data Ingestion Framework with Notebooks on Microsoft Fabric

Ingesting and curating data from your various systems into Microsoft Fabric can get quite overwhelming when you realize that there are way too many tables and data structures to bring in. This is when you need to turn to a repeatable framework that would provide you with an automated mechanism to ingest and curate those many tables or data structures, thereby saving you time and effort.
In this session you will learn how you could use good old notebooks in Microsoft Fabric along with some of its friends to build a data ingestion framework. Whilst there are many good approaches to building such a solution, here you will see how reusability and configurability can go a long way to achieve the desired results with performance.

Designing a Single Source of Truth with Power BI (and Fabric)

You may have noticed certain problems that come along when you implement self-service business intelligence at scale—duplicate datasets, redundant reports, and siloed content spread across workspaces and departments. As Power BI adoption grows, so does the clutter. This is often the result of non-standardized practices and a lack of governance.

In this session, we’ll explore how to bring structure and scalability to your BI landscape using the right mix of tools and techniques—based on your data complexity, organizational needs, and other factors. From dataflows and DirectQuery to semantic models, warehouses, and lakehouses, I’ll walk you through when, why and how to use each option as you work toward building a trusted, reusable, and scalable single source of truth for data-driven insights.

Putting up a BI Architecture with Discipline and Flexibility using Power BI

Power BI offers flexibility to do business intelligence; it allows users from all levels of an organization to perform their own analytics; to make it relevant to them. However, with flexibility comes chaos, and soon you will find a lot of clutter: Dozens of workspaces, messy naming, duplicate datasets, swarms of reports that overlap workspaces, almost non-existent security, while each author swearing that their version of the dataset has the absolute truth.
Chaos needs to be put into order, while ensuring discipline and flexibility. In this session we will look at how a well-defined architecture for business intelligence can be set up using the various features offered by Power BI, along with appropriate guidelines. And of course there will be demos.

Crafting Your Organization's Analytics Journey with Microsoft Fabric

You’ve invested in Microsoft Fabric—great. Now what?

With so many features available, it’s easy to get overwhelmed. Should you use a Lakehouse or a Warehouse? What’s the real difference? How does the medallion architecture fit in? What do you use to transform and engineer data—pipelines, dataflows, or notebooks? And how do you ensure your end users are empowered—not confused—by all the tools?

These are the kinds of questions I hear from customers all the time.

In this session, we’ll cut through the noise. I’ll walk through how to first align your business needs to a blueprint that fits your organization, then map that to Fabric’s capabilities, and design a practical, end-to-end journey—rather than just a solution—using Fabric. And we won’t forget user adoption: we’ll look at how to bake it in from the start, so what you build delivers value not just technically, but by enabling your end users to get what they need from it.

Power BI Data Modeling Workshop: From Requirements to a Flexible BI Semantic Model

BI is all about providing the right insights (to the right people at the right time), so that they could take informed decisions that better their business. The insights they need often change; the question asked today will morph into something else tomorrow. That is why the datasets that provide this information should be designed in a way that answering these questions become simple and flexible. Hence, why data modeling is given so much importance.

In this workshop we will start with analyzing a standard scenario, and identify business intelligence needs, define an architecture, then design your data model. We will then get into the business of querying the source data, transforming it and then building a star-schema based model based on your design. Along the way we will look at tips for effectively transforming different source structures of data, creating reusable transformation logic, defining measures, and configuring security.

Workshop Content
• Understanding and classifying requirements
• Defining an architecture
• Designing a data model
• Defining transformation logic
• Defining the semantic layer
• Defining and integrating security

Technology areas covered
• Power BI Desktop
• Power Query on Dataflows and Power BI Desktop
• DAX
• Row-level Security
• Power BI service

At the end of the workshop, you will be able to understand analytics requirements from business scenarios, design a data model solution and implement it.

Half day workshop.

Champion the Adoption of Power BI

Getting into Power BI is quite easy, since you can start free and do quite a bit of analytics just by following the plethora of learning resources out there. However, there comes a point where you might get stumped. It could be anything from your data model growing too big, to licensing issues, governance concerns, security problems, and architectural considerations. One of the main reasons that we’ve come to understand, is the lack of an architecture that fits your BI strategy, or a lack of strategy altogether. This in turn affects the adoption of Power BI as your BI solution, and eventually falls by the wayside.

In this table talk we will talk about how you could champion the adoption of Power BI. You may be evaluating Power BI, thinking of getting into Power BI, maybe you have indeed adopted Power BI for your department and want to take it to the entire organization, or you were evaluating, and then got stuck for whatever reason.

Talk to us, and let’s figure out together how you could champion the adoption of Power BI for your organization.

How-to: End-to-End Analytics with Azure Synapse Analytics

The traditional BI that we used to implement year after year in the 00s and early 10s gave way to big data and the cloud. Soon, we had cloud data warehouses and the modern data warehouse (MDW) architecture. But what is this, we are now talking of data lakehouses and Synapse? A new service where the siloed services of a MDW have all been put together? And end-to-end analytics service on the cloud. You've got to see this! Join me as I take you through a demo-driven session on how you could implement an end-to-end analytics solution using Azure Synapse Analytics.

Data Modeling in Power BI: Why it's important and How to do it

Power BI has fast become the primary choice for reporting and dashboarding for many. Yet, the desired flexibility and value it provides is lost on many occasions. It is also just considered a reporting and dashboarding tool, rather than an analytics and BI tool.
This session will focus on why data needs to be modeled, and how it can be done effectively, so that the true value of Power BI can be appreciated; not just as a reporting tool, rather as a provider of an analytics and BI solution.

First Cut: Delta Lakes and Data Lakehouses on Azure

Data lakes are here to stay. They are part and parcel of modern analytics solutions. But they do come with their own set of problems, including data reliability, query performance etc. To counter these we have enhancements and improvements that make data lakes better and easier to use. One of these is delta lake, and the concept of data lakehouses. Join this session to learn to build your first cut of a data lakehouse on Azure.

The Need for and Implementation of Power BI Dataflows

When you build different datasets for different purposes and scenarios, there always were certain tables that you needed across many of these datasets. While duplicating the transformation logic across each dataset, you might have often wondered why you would need to waste time doing the same thing over and over.
Enter Dataflows. In this session I will take you through why Dataflows is the best method to create reusable transformation logic, and how you would use it. But, that’s not all. Dataflows was created for other needs as well, such as for architectural abstraction, and dealing with large volumes of data. We will look at these needs too, and I will show you how Dataflows can be used to implement these needs.

Power BI Enterprise Architecture Workshop

Setting up a BI ecosystem for an organization goes beyond creating datasets and authoring reports. There are many aspects that needs to be looked at. Hence, the need for an enterprise BI solution. Such a solution needs to be looked at from a holistic perspective that touches on architecture, security, governance and adoption.
In this workshop we will start by looking at the concepts of BI, and how they fit into an enterprise scenario. We will primarily look at an architectural perspective of designing enterprise BI, and touch on security, governance and adoption, as well.

Workshop Content
• Business intelligence concepts and definitions from an enterprise perspective
• Enterprise BI architecture
• Sourcing an enterprise BI solution
• Modeling on an enterprise BI solution
• Security and governance considerations
• Adoption considerations

Technology areas covered
• Power BI Desktop
• Power Query on Dataflows and Power BI Desktop
• Power BI service
• Microsoft Purview

At the end of the workshop, you will be able to appreciate the need for an enterprise BI architecture and get started on setting up such a solution to suit your organizational needs.

Part 2 of the Power BI data modeling workshop. Half day workshop.

Data Lakehouse Workshop: End-to-end Analytics

The cloud gave rise to a plethora of concepts and technologies that revolutionized analytics. One of these was data lakes. Despite its promise, there were some areas that it could not properly capitalize on; especially that of certain data warehousing capabilities. Enter the data lakehouse.
In this workshop we will start by looking at the concept of a data lakehouse, architecture, and how you would set up one. We will then look at how you would engineer data and engineer analytics to set-up a platform for all types of analytics. Finally, we will look at different use cases of how to consume the data and analytics.

Workshop Content
• Data lakehouse concepts and architecture
• Architecture and typical data lakehouse set-up
• Data engineering on the lakehouse with Azure Databricks
• Analytics engineering on the lakehouse with Azure Synapse Analytics
• Data and analytics consumption use cases with Power BI

Technology areas covered
• Azure Databricks
• Azure Synapse Analytics
• Power BI

At the end of the workshop, you will be able to appreciate the need for a data lakehouse, and understand how an end-to-end analytics platform is implemented using the concept.

Full day workshop

Setting up Power BI Architectures with discipline and flexibility

Power BI is the business intelligence tool for everyone. For analysts, it allows for versatile reporting and self-service analysis. For end users, it allows for quick dashboarding and short time-to-insight. For IT, it allows for easy authoring and deployment of analytics.

With all this versatility and ease comes a lot of chaos, and if not handled from inception it can go out of hand in terms of dozens of workspaces, swarms of reports across these workspaces, ambiguous business measures, duplicate datasets, repetitive work, next-to-nothing security practices, multiple versions of the truth, and more.

Chaos needs to be put into order, while ensuring discipline *and* flexibility.

In this session we will look at how to set up a well-defined architecture for your organization’s BI needs. We will touch on the appropriate Power BI features for different scenarios, and cover areas of data collection, modeling, visualization, deployment, security and governance.

All of these, of course, with demos.

Data Governance for your Power BI Architecture integrated with Microsoft Purview

You have set-up your Power BI solution for your organization. You have datasets for various purposes and functions, you have reports, dashboards and scorecards running off them, and have started rolling it out across the organization. As your content grows, and as user adoption grows there will be an increasing number of questions coming to you.

Where can I find this KPI? Where does the data for this dashboard come from? How would I know which pieces of data are sensitive? What’s the definition of this metric? Who owns this dataset, I have a question for them? Where can I find sales measures for my dashboard? Which report is the correct one? Which datasets are certified for use?

You need to have one place you could go to find out the answers to these questions about your Power BI implementation. This is where data governance comes into the picture. Learn how you could set-up data governance for your Power BI architecture by integrating it with Microsoft Purview.

And of course, demos.

Effective monitoring of organizational goals with Metrics in Power BI

Every organization has key business objectives; these translate to goals across departments and employees. To effectively track these goals, promote employee accountability, and departmental alignment with the organizational objectives you need a data-driven solution, with the right functionality.

In this session we will look at how you could use Metrics in Power BI to design an effective solution to monitor an organization’s key business objectives.