Microsoft Sql Business Intelligence Advancement Workshop

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Microsoft Sql Business Intelligence Advancement Workshop – If you are designing solutions with Power BI, or using Power BI with other data platform components, for use in large organizations; How should you get started and what are the best practices to ensure success? I started a series of 12 blog posts on this topic back in July of 2020. If you can check all the boxes covered in these 12 topics, you are on your way to a successful Power BI solution. Almost all of the planned topics have been completed, but most of these posts are in the gap of two years of blogging history. I’ve linked to the original post and provided a brief summary to make each item as actionable as possible. This is a topic where I have an opinion and a passion for “doing it right”.

Before jumping in, understand the objectives, long and short term for the solution you are creating. Power BI lets you create reports, source and transform data very quickly. But, often at the expense of data management, data quality and long-term maintenance. Will your first project be a proof of concept or a full production solution? Here are some questions to consider as you head down this path:

Microsoft Sql Business Intelligence Advancement Workshop

Power BI is an incredible self-service reporting tool that can be used to quickly procure and analyze data sets. Enduring, enterprise-level solutions require scalable concepts. For convenience, Power BI Desktop allows one to quickly get data from source to presentation, but a durable solution consists of three layers: Data Transformation, Data Modeling & Data Presentation. Query & process changes, data modeling and report development can be implemented and managed by three different people in these special roles.

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Power Query is an amazing data transformation tool that, when used effectively, offers power and flexibility. Some transformation procedures that work with small data sets do not perform well with high-volume data sources. Understand your strengths and weaknesses, and learn to work with them. In particular, learn to use parameters, range filters and how to activate queries.

ETL work has always been a discipline and a process. Power Query makes it easier but a well-designed and manageable transformation query follows a well-defined pattern. After selecting a transformation that works effectively with your source data at the appropriate volume, use these guidelines:

Dataflows is an online implementation of Power Query, in the Power BI service. Instead of writing a query in Power BI Desktop and storing it in a PBIX file, the query may be designed in the browser, and then shared across multiple datasets. There are many good reasons to use Dataflows but they are not a silver bullet for every environment.

A common use case for Dataflows is to provide a standard set of transformations and data entities, when they are not defined in a central data warehouse. If you have an existing data warehouse, some functions of Dataflows may be redundant; However they also enable interesting features such as streaming datasets and AutoML. Start with the basics and learn to use PQ on the Desktop, then consider using Dataflows when needed.

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Data modeling is at the heart of Power BI and analytical reporting across data platforms. If you can get the data model right, many other things – such as calculating metrics and reporting functionality – will fall into place. Dimensioanl thinking is a paradigm shift requiring rethinking the way you transform and manage data. Before you convince yourself that frameworks are not necessary for your reporting needs, learn to use the dimensional design pattern, and to create rare exceptions. The star schema design will solve 95% of analytical reporting needs and 90% of reporting needs.

Put any new report in front of serious business users and they will ask “how do I know it’s right”? As data flows through the various stages of a BI solution, you need to be confident that the results are accurate and correct. Design your transformations, data models, measures and reports so that you can monitor the results and check each step that manages and manages data records and values. You can start checking the data in each table with a simple record count. Then build measures in layers that allow you or someone else to check and test the results to see the elements of each calculation. In large projects, you can model test data, to compare and verify reported measures with raw source values.

Having queries, data models, measures and reports all rolled into a single PBIX file makes development quick and easy. But, it also prevents more than one developer from working on a solution in pairs. Separating the data model design from the report design, by moving them into separate definition files, not only results in separation of tasks, but it also gives you the freedom to create multiple reports that share a central data model. Although there is some work effort when separating the model from the report, the benefits are immediately recognized. Community-supported tools make this task simple and easy to manage. Canal.

Power BI is an online service, hosted in the Microsoft Azure cloud. The most comprehensive option for publishing and sharing all the goodness of Power BI (reports, interactive graphics, dashboards, paginated reports, shared and certified datasets for self-service reporting) is to use the capabilities of Power BI Premium. When reports and datasets are bundled into workspace apps, they can be viewed and used by any number of users in your organization. Premium supports large datasets, auto-scaling and other features for enterprise-scale reporting and analysis. The monthly cost for Premium is a significant investment for serious customers and may not be attractive at first for small organizations. A more expensive option may be a better choice for small shops or those who want to test the waters before expanding their solution or audience of users.

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When should you use Paginated Reports instead of interactive Power BI reports? Paginated Reports is the new name for SQL Server Reporting Services (SSRS) since it was integrated into the Power BI premium service.

When planning a Power BI solution, how can we plan for scale and growth? We can do both “Self-service BI” and “Enterprise BI” with Power BI but the methods are different. I discuss this is a series of three chapters starting here in the development of a large Power BI data set – Part 1. In short, the enterprise design model may add a little time and effort to your project but the return on investment will be the solution in the future. Will handle large amounts of data and capacity to expand tolerance as the need arises.

The Power BI service can handle a lot of data, but just because your data source is large doesn’t mean your Power BI dataset will take up a lot of space. If the data format is designed effectively, even terabytes of source data usually translates into megabytes, or a few gigabytes of most data set storage.

The topics of DevOps (operational development) and CI / CD (continuous integration / continuous delivery) for BI solutions are diverse topics. In short, we can perform CI / CD for Power BI projects, but the dynamics are different from application development projects, for various reasons. Depending on the size and scale of the project, the method can be quite simple if you follow some basic instructions or complex if you want to use a strict DevOps process. The good news is that it can be done but there is no need for a one-size-fits-all solution.

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I begin by classifying projects by scope and size and then recommend methods for small to medium-sized projects with simple team management, versions and requirements; In this article: DevOps & CI/CD for Power BI. In the embedded YouTube video, I demonstrate using Teams and SharePoint with query parameters in Power BI to manage files in a simple code repository for a small project. I also show you how to set up a GitHub repository for the same purpose.

This topic introduces the key elements that are important for an information management organization. Business for reporting and analysis… Data Governance. It is not a tool or something that IT develops and installs. Information management represents a change in organizational culture and a set of Policies to determine ownership and drive decision-making. The following schematic diagram is a small piece of the governance puzzle.

From the perspective of data users, consider the following options and decision points. This flowchart calls out three different possible use cases for report users/analysts:

This guide mainly addresses the first two use cases, to create data models that support enterprise reports; or data formats that can be used by savvy business users to search, search, and create their own reports.

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