Sql Web Server Business Intelligence Advancement

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Sql Web Server Business Intelligence Advancement – If you have designed a solution using Power BI or are using Power BI with other data platform components for a large organization. How to get started and what are the best ways to ensure success? I started a series of 12 blog posts on this topic starting in July 2020. If you check all the boxes listed in these 12 topics, you’re well on your way to a successful BI solution. Almost all of the planned topics have been completed, but most of these posts are under two years of blog history. I’ve linked to the original posts and provided a short summary to make each item as moving as possible. These are my opinions and my passion for “doing it right”.

Before you jump in, understand the purpose, long-term, and short-term goals of the solution you’re building. Power BI lets you quickly create reports, resources, and data. But, often at the expense of maintaining data governance, data quality, and long-term sustainability. Will your first project be a proof of concept or fully production ready? Here are a few questions to consider as you go down this path:

Sql Web Server Business Intelligence Advancement

Power BI is an incredible self-service reporting tool that can be used to quickly acquire and analyze a set of data. Sustainable, enterprise-scale solutions require scalable thinking. For convenience, Power BI Desktop allows one to get a quick presentation of source data, but a robust solution consists of three layers: data transformation, data modeling, and data presentation. Query and change processes, data modeling, and report development are executed and managed by these three different people.

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

ETL work has always been a routine and process discipline. Power Query makes this easy, but well-designed and manageable transformation queries follow well-defined patterns. Once you’ve chosen the transformations that work best with the right amount of source data, follow these guidelines:

Dataflow is an online implementation of Power Query in the Power BI service. Queries can be designed in a browser and then shared across multiple databases, rather than writing queries in Power BI Desktop and storing them in PBIX files. There are many good reasons to use Dataflows, but they are not a silver bullet solution for every environment.

A common use case for Dataflows is to provide standard transformations and data entities when not defined in a standard database. If you have an existing database, some data streams may be redundant. However they enable interesting features like databases and AutoML. Start with the basics and learn how to use PQ on the desktop, then consider using Dataflows when needed.

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Data models are the core of Power BI and analytics reporting in Data Platform. If you get the data model right, a lot of other things – like measurement calculations and reporting capabilities – will work. Dimensioanl thinking is paradigmatic and requires rethinking the way you transform and manage data. Before you convince yourself that a star scheme is unnecessary for reporting, learn to apply standard model design patterns and rule out rare cases. Star Design solves 95% of analytical reporting 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 true?” As data flows through multiple stages of a BI solution, you need to be confident that the results are accurate and correct. You can design your transformations, data models, measures, and reports, track results, and validate every step of processing and controlling data records and values. You can start validation by simply recording the data in each table. Then, create layers where you or someone else can view each component of the survey and calculate the test results. In large projects, you can create test data models, compare and validate raw values ​​and reported metrics.

Queries, data models, metrics, and reports are all bundled into a single PBIX file, making development fast and easy. However, it also prevents multiple developers from working on a concurrent solution. Separating the data model design from the report design by moving them into separate definition files not only helps to separate the work, but also provides the freedom to create multiple reports that share a central data model. Although there is some work involved in separating the models from the reports, the benefits are immediate. Community-supported tools make this task relatively simple and manageable.

Power BI is a web service hosted in the Microsoft Azure cloud. The most comprehensive option for publishing and sharing all the goodness of Power BI (documents, interactive visualizations, dashboards, paginated reports, shared and certified data for self-reporting) is to use Power BI Premium capabilities. Once reports and data sheets are linked to the Workspace app, they can be viewed and used by any user in your organization. Premium supports big data analytics, auto-scaling, and a number of other features for enterprise-scale reporting and analytics. The monthly cost of Premium is a substantial investment for heavy customers and may not be attractive to smaller organizations at first. Less expensive options may be a better choice for small shops or those who need to test the waters with their user audience before expanding their solution.

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

When planning a Power BI solution, how do we plan for scale and growth? We can do “self-service BI” and “enterprise BI” with Power BI, but the approach is different. Here I discuss a series of three posts that started on developing a large BI database – in short, following an enterprise design pattern may add some time and effort to your project, but the return on investment will be future solutions. Handles more 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 big doesn’t mean your Power BI data table takes up a lot of space either. If the data model is designed effectively, even terabytes of source data typically translate into megabytes, or more often several gigabytes of data 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 implement CI/CD for Power BI projects, but the dynamics are different for application development projects, for various reasons. Depending on the size and scope of the project, if you need to apply strict DevOps processes, the approach can be relatively simple if you follow some basic guidelines or complex operations. The good news is that it can be done, but there isn’t necessarily a one-size-fits-all solution.

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I’ll start by categorizing projects by size and scope, and then introduce an approach for small and medium-sized projects with simple team management, publishing, and deployment needs. 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 documents in a simple code repository for small projects. I’ll also show you that I’ve created a GitHub repository for the same purpose.

This topic introduces a key element that is critical to any organization managing business data for reporting and analysis… data governance. It’s not a tool or thing that IT develops and installs. Data governance represents a change in organizational culture and a set of policies that determine ownership and decision-making. The flowchart below is a small piece of the “management puzzle”.

From a data user perspective, consider the following options and decision points. This flowchart invokes three different usage methods for the report user/analyst:

This guide primarily addresses the first two use cases, creating a data model to support enterprise reporting. or data models that intelligent business users use to visualize, explore, and create their own reports.

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& Workspace and Database Recovery Techniques Aaron Nelson Ad-hoc Report Column Addition Control Albert Ferrari

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