Paul Turley's SQL Server BI Blog
sharing my experiences with the Microsoft data platform, SQL Server BI, Data Modeling, SSAS Design, Power Pivot, Power BI, SSRS Advanced Design, Power BI, Dashboards & Visualization since 2009
Business users often need to know how fresh the data is that they see on a Power BI report. This requirement can be interpreted to mean either “when was the last date & time that the source data was updated?” or “when was the last date & time that the data model was refreshed?”
I’ve used a few different approaches to record the actual date and time, but all generally using the same technique.
There is no secret about this. If you do any legitimate research about Power BI (reading blogs, books or training from reliable sources), you will quickly learn that a lot of basic functionality requires a dimensional model, aka “Star Schema”. This is a hard fact that every expert promotes, and self-taught data analysts either have learned or will learn through experience. So, if everyone agrees on this point, why do so many resist this advice?
Perspective is everything. I didn’t understand why getting to the star schema was so out of reach so often until I was able to see it from another perspective. There are a few common scenarios that draw source data into different directions than an ideal dimensional model.
DevOps isn’t difficult to implement for small and medium-scale projects, and simple things like managing version control in a code repository can save hours of lost time. Organization who are accustomed to managing large application development initiatives might expect to have a fully automated build and deployment process in concert with an Agile delivery process, managed with specialized tools like Jira, GitHub and Azure DevOps.
Power BI is architected to consume data in a dimensional model, with narrow fact tables and related dimensions. Introducing a big, wide table in a tabular model is extremely inefficient. It takes up space and memory resources, impacts performance, and complicates measure coding. Flattening records into a flat table is one of the worst things you can do in Power BI and a common mistake made by novice Power BI users.
Table partitioning has long been an important task in semantic model design using SQL Server Analysis Services. Since SSAS Tabular and Power BI models are built on top of the SSAS architecture, the pattern of partitioning remains the same as it has been for twenty years. However, the specific methods for implementation have been fine-tuned and improved. The reasons for partitioning large fact tables mainly include:
Improve refresh speed,
Prevent reloading historical records,
Capture updated history,
Reduce database resource load
You don’t have to have massive tables to benefit from partitioning. Even tables with a few hundred thousand records can benefit from partitioning, to improve data refresh performance and to detect source data changes. There is little maintenance overhead, so the benefits usually outweigh the cost, in terms of effort and management.
When planning a Power BI solution, how can we plan for scale and growth? Like any technology, Power BI has limits but Power BI can manage a surprising large value of analytic data. We also have tools and companion technologies capable of handling workloads of data that alongside Power BI.
The Right Tool for the Job
You wouldn’t use a screwdriver to hammer a nail and you wouldn’t use a chainsaw to assemble an Ikea cabinet; so why would you use Power BI Desktop to create a detailed shipping manifest? It’s not the right tool for the job. The Power BI platform contains tools that are appropriate to use for different reporting scenarios and using them together will yield far better results than forcing tools to behave differently than they were intended.
Colleagues: if I have sent you to this post, it is because I respect you and want to make sure…
Even for small, informal BI projects, shaping the data into a dimensional model alleviates complexity, speeds up slow calculations and reduces the data model storage size. I conclude this post by reviewing seven data architectures and the data shaping methods with different degrees of scale.
If you are in the Chicago area and haven’t already registered for the Data Insights Summit, please join me. I…
A Gantt chart is an excellent example of where Paginated Report & SSRS were an ideal choice for the purpose. It is a running list of activities with the duration for each displayed as a horizontal bar depicting the beginning and ending day along a horizontal scale. The challenge is that this is not a standard chart type in either Power BI or SSRS/Paginated Reports. Furthermore, project planners may prefer to see activities as rows in the format of a printed page.
I started a series of blog posts back in 2020 about best-practice guidelines for planning and designing enterprise reporting solutions with Power BI. To make the topics covered in this series of posts easier to find and follow, they are listed on this page: Doing Power BI The Right Way – for Enterprise Reporting | Paul Turley’s SQL Server BI Blog which you can access from the main menu on the blog. We have a few more topics to go so check back and subscribe for notifications.
Part of the the series: Doing Power BI the Right Way When you sign-up for the Power BI service at PowerBI.com…
When should we use Power BI vs Paginated Reports for our reporting needs? You can use either tool to create different styles of reports for different purposes, but should you? Just because you CAN doesn’t mean you SHOULD.
The following diagram depicts the relative capabilities and design purpose for these two reporting tools. See the overlap? On the left, you see that Power BI is optimized and best used to create interactive, visual reports, scorecards and dashboard-style reports. Contrast this with Paginated Reports on the right side, which is best used for “list-type” reports that continuously flow across multiple pages. SSRS/Paginated Reports was architected and works ideally with SQL queries, where filter values are passed into the query using parameters.
The December 2021 Power BI Desktop update introduced a long-awaited upgrade to the partitioning and Incremental Refresh feature set. The update introduces Hybrid Tables, a new Premium feature that combines the advantages of in-memory Import Mode storage with real-time DirectQuery data access; this is a big step forward for large model management and real-time analytic reporting.
Last year I wrote this post about the new composite model feature in Power BI that enables datasets to be…
