Microsoft Sql Web Server Business Intelligence Advancement Workshop Download And Install 2008 – If you design solutions with Power BI or use Power BI together with other information platform components for use 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 back in July 2020. If you can tick all the points in these 12 topics, you’re on your way to a successful Power BI solution. Almost all planned topics were completed, but most of these posts are in the gap of two years of blog history. I’ve linked to the original posts and provided a short summary to make each item as functional as possible. These are subjects that I have opinions about and a passion to “do it right”.
Before you start, understand the purpose of the solution you’re building, the long-term and short-term goals. With Power BI, you can create reports, exit and transform data very quickly. But often at the expense of data governance, data quality and long-term maintainability. Is your first project a proof of concept or a full-scale production-ready solution? Here are some questions to consider when going this route:
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Power BI is an incredible self-service reporting tool that can be used to quickly acquire and analyze a set of data. Sustainable, enterprise-level solutions require a scalable mindset. For convenience, Power BI Desktop allows one person to get from source data to presentation quickly, but sustainable solutions consist of three layers: Data Transformation, Data Modeling & Data Presentation. Queries and conversion processes, data models and report development 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, when used effectively, offers power and flexibility. Some transformation steps that work with small data sets do not work very well with large data sources. Understand your 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 easier, but well-designed and manageable transformation queries follow well-defined patterns. Once you’ve selected transformations that work effectively with your source data in the right amount, follow these steps:
Dataflows are the online implementation of Power Query in the Power BI service. Instead of building queries in Power BI Desktop and saving them to a PBIX file, queries can be designed in a web browser and distributed across multiple datasets. There are many good reasons to use data streams, but they are not the best solution for all environments.
A common use of data streams is to provide a standardized set of transformations and datasets when they are not already defined in a central repository. If you have an existing data warehouse, some Dataflows functionality may be unnecessary; However, they also enable interesting features such as streaming datasets and AutoML. Start with the basics and learn how to use PQ on Desktop. Then consider using data streams if needed.
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Data modeling is at the core of Power BI and analytical reporting in the entire data environment. If you get the data model right, many other things – like metrics calculations and reporting functions – will fall into place. Dimensional thinking is a paradigm shift that requires a rethinking of data transformation and management. Before you convince yourself that a star schema is unnecessary for your reporting needs, learn how to apply dimensional model design patterns and make rare exceptions. Star schema design covers 95 percent of analytical reporting requirements and 90 percent of reporting needs.
Present a new report to a serious business user and they will ask “how do I know this is correct”? As data passes through the multiple stages of a BI solution, you need to be sure that the results are correct and accurate. Design your transformations, data models, measures, and reports so that you can trace results and validate all steps that process and modify data records and values. You can start validating the data in each table with simple record calculations. Then build measurements to levels that allow you or someone checking and testing the results to see all parts of the calculation. In large projects, you can create a test data model that allows you to compare and validate report metrics with raw source values.
When the queries, data model, measurements and reports are gathered in one PBIX file, development is fast and easy. But it also prevents more than one developer from working on the solution at the same time. Separating the data model design from the report design by moving them to separate configuration files allows separation of work, but also gives the freedom to create multiple reports that share a central data model. Although there are some trade-offs in separating models and reports, the benefits are immediate. Community supported tools make this task quite simple and manageable.
Power BI is an online service hosted in the Microsoft Azure cloud. The most comprehensive option for publishing and sharing all the good features 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 bundled into a workspace application, everyone in your organization can view and access them. Premium supports large datasets, auto-scaling, and many other features that enable enterprise-level reporting and analytics. The monthly cost of Premium is a serious investment for a serious customer and may not initially be attractive to smaller organizations. Cheaper options may be a better choice for smaller shops or those who need to test the waters before expanding the solution or 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 with Power BI premium service.
When designing 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 discuss this in a series of three posts starting here Developing Large Power BI Datasets – Part 1. In short, following enterprise design patterns may add a little time and effort to your project, but that ROI is a future-proof solution. handles larger amounts of data and features that extend durability as 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 also take up a lot of space. If the data model is designed efficiently, even terabytes of source data are usually converted into megabytes or at most a few gigabytes of data set storage space.
The topic 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 than for application development projects for several reasons. Depending on the size and scope of the project, the approach can be quite simple if you follow some basic guidelines, or complex if you need to apply strict DevOps processes. The good news is that it can be done, but there may not be a one-size-fits-all solution for all projects.
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I’ll start by categorizing projects by scope and scale, then introduce an approach for small to medium-sized projects with simple team management, versioning, and deployment needs. in this post: DevOps & CI/CD for Power BI. In the embedded YouTube video, I demonstrate using Teams and SharePoint and Power BI query parameters to manage files in a simple code repository for small-scale projects. I also suggest setting up a GitHub repository for the same purpose.
This topic introduces an important element critical to any organization that manages business data for reporting and analysis… Data Management. It’s not a tool or something that IT develops and installs. Data Governance represents a change in organizational culture and practices for determining ownership and making decisions. The following flowchart is a small piece of the Governance puzzle.
Consider the following options and decision points from the perspective of the data user. This flowchart shows three different possible use cases for the report user/analyst:
This guide mainly covers the first two use cases for creating data models that support business reports. or data models that savvy business users can use to browse, explore, and create their own reports.
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