Sql Business Intelligence Advancement Workshop 2012 – If you design solutions with Power BI or use Power BI together with other components of a data platform to be used in a large organization; 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 in July 2020. If you can check all the boxes covered in these 12 topics, you’re on your way to a successful Power BI solution. Almost all the planned topics are done, but most of these posts are in the abyss of two years of blogging history. I’ve linked to the original posts and provided a brief synopsis to make each item as actionable as possible. These are subjects that I have an opinion on and a bit of a passion to “do right”.
Before jumping in, understand the purpose, long-term, and short-term goals of your solution. Power BI can let you build reports, sources, and transform data very quickly. However, often at the cost of data management, data quality and long-term storage. Will your first project be a proof of concept or a full production solution? Here are some questions to consider when going this route:
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Power BI is an excellent self-service reporting tool that can be used to quickly acquire and analyze data sets. Sustainable, enterprise-scale solutions require scalable thinking. For convenience, Power BI Desktop allows one person to quickly get from source data to presentation, but robust solutions consist of three layers: Data Transformation, Data Modeling, and Data Presentation. Query and transformation processes, data models, and report generation can be performed and managed by three different people in these specialized roles.
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Power Query is an amazing data transformation tool that provides power and flexibility when used effectively. Some conversion steps that work with small data sets do not work well with high volume data sources. Understand your strengths and weaknesses and learn to work with them. In particular, learn how to use parameters, range filters, and how to enable query masking.
ETL work has always been a routine and process discipline. Power Query makes this easier, but well-designed and managed conversion queries are those that follow well-defined patterns. After choosing the transformations that work best with your source data at the right volume, apply these guidelines:
Dataflows are the online implementation of Power Query in the Power BI service. Instead of developing queries in Power BI Desktop and storing them in a PBIX file, queries can be designed in a web browser and then shared between multiple datasets. There are many reasons to use Dataflows, but they are not a silver bullet solution for every environment.
A common use case for Dataflows is to provide a standardized set of transformations and data objects when they are not already defined in a central data repository. If you have an existing data warehouse, some Dataflows functionality may be redundant; but they also enable some interesting features like data streams and AutoML. Start with the basics and learn to use PQ on the desktop, then consider using Dataflows as needed.
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Data modeling is at the heart of Power BI, and analytics reporting is at the heart of the entire data platform. If you get the data model right, many other things, such as measurement calculations and reporting functions, will simply fall into place. Dimensioanl thinking is a paradigm shift that requires rethinking the way we transform and manage data. Before you convince yourself that a star schema is unnecessary for your reporting needs, learn to use dimensional model design patterns and create rare exceptions. A star schema design will solve 95% of analytical reporting needs and 90% of reporting needs.
Put any new report in front of a serious business user and they’ll ask “how do I know it’s right”? As data flows through the multiple stages of a BI solution, you need to be confident that the results are valid and accurate. Design your transformations, data model, metrics, and reports so you can track results and validate each step that processes and processes records and data values. You can start validating the data in each table with simple calculations. Then, create measurements in layers that allow you or someone to see the results of the test and test to see each item in the calculation. In large projects, you can create a test data model to compare and validate report metrics with raw source values.
Having queries, data model, metrics, and reports bundled into a single PBIX file makes development fast and easy. However, it also prevents more than one developer from working on a solution together. Separating data model design from report design by moving them into separate definition files not only allows you to separate tasks, but also gives you the freedom to create multiple reports that share a central data model. Although there is some labor overhead when separating models from reports, the benefits are immediately realized. Community supported tools make this task very simple and easy to manage.
Power BI is an online service hosted on the Microsoft Azure cloud. The most complete option for publishing and sharing all the benefits of Power BI (reports, interactive visualizations, dashboards, paginated reports, shared and certified datasets for self-service reporting) is to use Power BI Premium capabilities. When reports and datasets are collected in a single workspace application, they can be viewed and used by any number of users in your organization. Premium supports big data sets, auto-scaling, and a host of other features that support enterprise-scale reporting and analytics. The monthly cost for 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 shops or those testing the waters before scaling their solution or user audience.
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When should you use Paginated Reports rather than interactive Power BI reports? Paginated reporting is the new name for SQL Server Reporting Services (SSRS) as it is 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 approach is different. I’ll be discussing this in a three-part series starting here in Building Big Power BI Collections – Part 1. In short, following enterprise design patterns may add some time and effort to your project, but the return on investment will be a future solution. will handle larger volumes of data and capabilities that extend durability should the need arise.
The Power BI service can handle a lot of data, but just because your data source is large doesn’t mean that your Power BI dataset will also take up a lot of space. If the data model is designed effectively, even terabytes of source data typically translate into megabytes or at most a few gigabytes of data set storage.
The subject of 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 the dynamics are different from application development projects for various reasons. Depending on the size and scope of the project, the approach can be very 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 there isn’t necessarily a one-size-fits-all solution for all projects.
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I’ll start by classifying projects by size and scope, and then an approach for small to medium-sized projects that have simple team management, versions, and deployments; in this post: DevOps & CI/CD for Power BI. In the embedded YouTube video, I demonstrate using Teams and SharePoint along with query parameters in Power BI to manage files in a simple code repository for small-scale projects. I will also show you how to create a GitHub repository for the same purpose.
This topic introduces an important element for any organization that manages business data for reporting and analysis… Data management is critical. It’s not a tool or something that IT develops and installs. Data governance represents a change in organizational culture and a set of policies for determining ownership and decision making. The following table diagram is a small piece of the Governance puzzle.
From the data user’s perspective, consider the following options and decision points. This diagram calls out three possible use cases for a report user/analyst:
This guide mainly addresses the first two use cases for the data models that support enterprise reporting; or data models that can be used by business users to view, explore, and create their own reports.
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