Ms Sql Web Server Business Intelligence Advancement Workshop Download And Install – If you’re designing a solution with Power BI for use in a large organization, or using Power BI with other data platform components; 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 were completed, but most of these posts lie in the abyss of two years of blogging history. I’ve linked the original posts and provided a brief summary to make each item as actionable as possible. These are topics I have opinions about and a passion for “doing it right”.
Before jumping in, understand the purpose, long-term and short-term goals of the solution you are building. Power BI can enable you to create reports, source, and transform data very quickly. But, often at the expense of data governance, data quality and long-term maintainability. Will your first project be a proof of concept or a solution ready for full-scale production? Here are some questions to consider as you start down this path:
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Power BI is an incredible self-service reporting tool that can be used to quickly ingest and analyze a set of data. Sustainable, enterprise-scale solutions require a scalable mindset. For convenience, Power BI Desktop allows a person to quickly get from source data to presentation, but a sustainable solution consists of three layers: data transformation, data modeling, and data presentation. Query and transformation processes, data models, and report development may be performed and managed by three different people in these specific roles.
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Power Query is an amazing data transformation tool that, when used efficiently, provides power and flexibility. Some transformation steps that work with small sets of data do not work very well with high volume data sources. Understand strengths and weaknesses and learn to work with them. In particular, learn how to use parameters, range filters, and enable query folding.
ETL work has always been a discipline of routine and process. Power Query makes this easy but well-designed and manageable transformation queries are those that follow well-defined patterns. After choosing transformations that work with your source data in the right amount of efficiency, apply these guidelines:
Dataflow is an 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 Dataflow but they are not a silver-bullet solution for every environment.
A common use case for dataflow is to provide a standardized set of transformations and data entities when they are not already defined in a central data warehouse. If you have an existing data warehouse, some Dataflow functionality may be unnecessary; However they also enable some interesting features like streaming datasets and AutoML. Start with the basics and learn how to use PQ in desktop, then consider using Dataflow when needed.
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Data modeling is at the heart of Power BI and analytical reporting across the entire data platform. If you can get the data model right, many other things – like measure calculations and reporting functionality – will simply fall into place. Dimensional thinking is a paradigm shift that requires rethinking the way data is transformed and managed. Before you convince yourself that the star schema is unnecessary for your reporting needs, learn to apply dimensional model design patterns and make the rare exceptions. The star schema design will address 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 this is right”? As data passes through multiple stages of a BI solution, you need to be confident that the results are valid and accurate. Design your transformations, data models, measures, and reports so that you can trace results and validate each step you take to handle and manipulate data records and values. You can start validating the data in each table with simple record counts. Then, create measurements in layers that allow you or someone else to check and test the results to see each component of the calculation. In larger projects, you can create a test data model to compare and verify report measures with raw source values.
Rolling queries, data models, measurements, and reports all into a single PBIX file makes development quick and easy. But, it also prevents more than one developer from working together on a solution. Separating data model design from report design, by moving them into separate definition files, not only enables separation of work but it also gives you the freedom to create multiple reports that share a central data model. Although there is some work-effort trade-off when separating models from reports, the benefits are immediately felt. 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 for publishing and sharing all the goodness of Power BI (reports, interactive visuals, dashboards, paginated reports, shared and authenticated datasets for self-service reporting) is to use the Power BI Premium capability. Once reports and datasets are bundled into the Workspace app, they can be viewed and used by any number of users in your organization. Premium supports large datasets, auto-scaling, and many other features that enable enterprise-scale reporting and analytics. The monthly cost of the premium is a large investment for serious customers and may not be attractive at first for smaller organizations. Less expensive options may be a better choice for smaller shops or those who need to test the waters before expanding a solution or their user audience.
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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 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. What I discuss is a series of three posts that starts here in Developing Large Power BI Datasets – Part 1. In short, the following enterprise design patterns may add a little time and effort to your project but the return on investment will be a future-proof solution that will handle large amounts of data and capabilities that will increase durability when needed.
The Power BI service can handle a lot of data, but just because your data sources are large doesn’t mean that your Power BI datasets will also take up a lot of space. If the data model is efficiently designed, even terabytes of source data will typically translate into megabytes, or at most a few gigabytes, of dataset storage.
The topic of DevOps (Development Operations) and CI/CD (Continuous Integration/Continuous Delivery) for BI solutions is a multifaceted topic. In short, we can apply CI/CD to Power BI projects, but the dynamics differ from application development projects for several reasons. Depending on the size and scale of the project, the approach can be quite 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 solution for all projects.
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I start by classifying projects by scope and scale and then present an approach for small to medium-scale projects that require simple team management, versioning, and deployment; In this post: DevOps and 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 small-scale projects. I also demonstrate setting up a GitHub repository for the same purpose.
This topic introduces a key element that is important for any organization managing business data for reporting and analysis…data governance. It is not a tool nor something that IT develops and installs. Data governance represents an organizational culture shift and a set of policies to define ownership and decision making. The following flowchart diagram is a small piece of the governance puzzle.
From the data user’s perspective, consider the following options and decision points. This flowchart illustrates three different possible use cases for a report user/analyst:
This guide primarily addresses the first two use cases for creating data models that support enterprise reports; Or data models that savvy business users can use to browse, explore, and create their own reports.
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