Sql Web Server Business Intelligence Advancement Workshop 2016 – If you are designing solutions with Power BI for use in a large enterprise or using Power BI with other data platform components; How should you start and what are the best practices to ensure success? I started a series of 12 blog posts on this topic in July 2020. If you tick all the boxes on these 12 topics, you’re on your way to a successful Power BI solution. Almost all of the planned topics are complete, but most of these posts are on the abyss of two years of blogging history. I’ve linked to the original posts and provided a brief summary to make each item as actionable as possible. These are topics I have ideas for and some passion for “doing it right”.
Before jumping in, understand the purpose of the solution you are building, and its long-term and short-term goals. Power BI can enable you to generate reports, provision and transform data very quickly. But often at the expense of data governance, data quality and long-term sustainability. Will your first project be a proof-of-concept or a full-scale production-ready solution? Here are some questions to consider as you go down this path:
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Power BI is an incredible self-service reporting tool that can be used to quickly procure and analyze a range of data. Durable, enterprise-scale solutions require a scalable mindset. For simplicity, Power BI Desktop allows one to quickly move from source data to presentation, but durable solutions consist of three layers: Data Transformation, Data Modeling, and Data Presentation. Inquiry & transformation processes, data models and report development can be run and managed by three different people in these specific roles.
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Power Query is a great data transformation tool that provides power and flexibility when used efficiently. Some transformation steps that work with small datasets don’t work well with high-volume data sources. Understand strengths and weaknesses and learn to work with them. Specifically, learn how to enable parameters, using range filters, and query folding.
ETL business has always been a routine and process discipline. Power Query makes it easy, but well-designed and manageable conversion queries are those that follow well-defined patterns. Once you’ve selected the right volume of conversions that work efficiently with your source data, follow these guidelines:
Dataflows are the online implementation of Power Query in the Power BI service. Instead of writing queries in Power BI Desktop and storing them in a PBIX file, queries can be designed in a web browser and then shared across multiple datasets. There are many good reasons to use Dataflows, but they are not a magic wand solution for every environment.
A common use case for dataflows is to provide a standardized set of transformations and data entities when they are not yet defined in a central data warehouse. If you have an existing data warehouse, some Dataflows functions may be redundant; but they also enable some interesting features like streaming datasets and AutoML. Start with the basics and learn to use PQ on Desktop, then consider using Dataflows as needed.
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Data modeling is at the heart of Power BI and analytical reporting across the data platform. If you can set up the data model correctly, many other things will fall into place, such as measurement calculations and report functionality. Dimensional thinking is a paradigm shift that requires you to rethink the way you transform and manage data. Learn to apply dimensional model design patterns and make rare exceptions before you convince yourself that a star chart is unnecessary for your reporting needs. The star chart design will meet 95% of analytical reporting needs and 90% of reporting needs.
Put any new report in front of a serious business user and they will ask, “How do I know this is true”. As data moves through multiple stages of a BI solution, you need to make sure the results are valid and accurate. Design your conversions, data model, metrics, and reports so that you can track results and validate every step that processes and manages data records and values. You can start validating data in each table with simple record counts. Then create metrics in layers that allow you or someone to see each component of the calculation by checking and testing the results. For large projects, you can create a test data model to compare and validate report metrics with raw source values.
The collection of queries, data model, metrics, and reports in a single PBIX file makes development fast and easy. But it also prevents multiple developers from working together on the solution. Separating data model design from report design by moving them into separate definition files not only ensures separation of work, but also gives you the freedom to create multiple reports that share a central data model. While there are some labor-effort trade-offs when separating models from reports, the benefits are immediately noticeable. Community-supported tools make this task quite simple and easy to manage.
Power BI is an online service hosted in the Microsoft Azure cloud. The most comprehensive option to publish and share all the benefits of Power BI (reports, interactive visuals, dashboards, Paginated reports, shared and certified datasets for self-service reporting) is to use the Power BI Premium capacity. Once the reports and dataset are collected in a workspace application, they can be viewed and used by any number of users in your organization. Premium supports large datasets, autoscaling, and a host of other features that support enterprise-scale reporting and analytics. The monthly cost of Premium is a significant investment for serious customers and may not be attractive to small organizations at first. Cheaper options may be a better choice for smaller stores or those who need to test the water before growing the solution or user base.
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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) as it is integrated with the Power BI premium service.
How can we plan for scale and growth when planning a Power BI solution? We can do both “self-service BI” and “enterprise BI” with Power BI, but the approach is different. I’m talking about this in a three-post series that started in Developing Large Power BI Datasets – Part 1. In short, the following corporate design patterns can save your project some time and effort, but this return on investment will be a future-proof solution that will handle larger volumes of data and capabilities that increase resilience as needs arise.
The Power BI service can handle a lot of data, but just because your data sources are large doesn’t mean your Power BI datasets will also take up a lot of space. Even terabytes of source data are often translated into megabytes, or at most a few gigabytes of dataset storage, if the data model is designed efficiently.
DevOps (Development Operations) and CI/CD (Continuous Integration / Continuous Delivery) for BI solutions is a multifaceted topic. In short, we can implement CI/CD for Power BI projects, but dynamics is different from application development projects for several reasons. Depending on the size and scale of the project, the approach can be fairly simple if you follow some basic guidelines, or complex if you need to implement strict DevOps processes. The good news is that it can be done, but it doesn’t have to be a one-size-fits-all solution to fit all projects.
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I start by categorizing projects by scope and scale, and then offer an approach for small to medium projects with simple team management, versioning, and deployment requirements; in this post: DevOps and CI/CD for Power BI. In the embedded YouTube video, I demonstrate using query parameters in Power BI as well as Teams and SharePoint to manage files in a simple code repository for small-scale projects. I also demonstrate setting up a GitHub repository for the same purpose.
This topic introduces an important element that is critical to any organization that manages business data for reporting and analysis… Data Governance. It is not a tool or something that IT developed and installed. Data Governance represents a change in corporate culture and a set of policies to define ownership and guide decisions. The flowchart diagram below is a small piece of the Governance puzzle.
From a data user’s perspective, consider the following options and decision points. This flowchart shows three different possible use cases for a report user/analyst:
This guide mainly covers the first two use cases for building data models that support enterprise reports; or data models that can be used by savvy business users to browse, explore, and create their own reports.
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