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Microsoft Power BI Performance Best Practices
Microsoft Power BI Performance Best Practices

Microsoft Power BI Performance Best Practices: Learn practical techniques for building high-speed Power BI solutions , Second Edition

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Profile Icon Thomas LeBlanc Profile Icon Bhavik Merchant
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Arrow left icon
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€33.99
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Paperback Aug 2024 346 pages 2nd Edition
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Microsoft Power BI Performance Best Practices

Setting Targets and Identifying Problem Areas

Many people would consider report performance as the most critical area to focus on when trying to improve the speed of an analytics solution. This is largely true, because it is the most visible part of the system used by pretty much every class of user, from administrators to business executives. However, you will learn that there are other areas of the solution that should be considered if performance is to be managed comprehensively. For example, achieving good performance in the reporting layer might be of no consequence if the underlying dataset that powers the report takes a long time to be refreshed or is susceptible to failures due to resource limits or system limits being reached. In this case, users may have great-looking, fast reports that do not provide value because the data is stale.

The authors of this book have experienced the effects of poor report performance firsthand but have also enjoyed the fruits of improving...

Defining good performance

With the advent of ever-faster computers and the massive scale of processing available today by way of cloud computing, business users expect and demand analytical solutions that perform well. This is essential for competitive business decision-making. Business Intelligence (BI) software vendors echo this need and tend to promise quick results in their sales and marketing materials. Their expectations mean that it is uncommon to find users getting excited about how fast reports are or how fresh data is because it is something implicit to them having a positive experience.

Conversely, when users must wait a long time for a report to load, they are quite vocal and tend to escalate such issues via multiple channels. When these problems are widespread, they can damage the reputation of both software platforms and the teams involved in building and maintaining those solutions. In the worst possible case, users may refuse to adopt these solutions and management...

Considering areas that could slow you down

The next step in our performance management journey is to understand where time is spent. A Power BI solution is ultimately about exposing data to a user and can be thought of as a flow of data from source systems to data stores. Through these various components, eventually, the visual will reach a user through a browser or mobile device. A simplified view of a Power BI solution is presented in the following figure.

Figure 1.4 – Power BI solution overview

Figure 1.4 – Power BI solution overview

Here, we will briefly focus on the different paths of a typical solution to explain why each piece has important considerations for users and the effect poor performance can have on each process. Some of these areas will be covered in more detail in Chapter 2, Exploring Power BI Architecture and Configuration.

Connecting data sources

The following diagram highlights the areas of the solution that are affected when data sources and connectivity methods do not perform well:

Figure 1.5 – Areas affected by data source and connectivity issues

Figure 1.5 – Areas affected by data source and connectivity issues

Import mode

When using Import mode datasets, developers can experience sluggish user interface responsiveness when working with Power Query or M on the desktop. In extreme cases, this can extend data transformation development from hours to days. Once the solution is deployed, problems in this area can cause refresh times to time out or fail. The Power BI service has a refresh limit of 2 hours, while Premium and Fabric capacities can extend this to 5 hours. Any refresh hitting this limit will be canceled by the system.

DirectQuery mode

DirectQuery mode leaves the data at the source and needs to fetch data and process it for measures when users interact with a report page. Issues with this part of the configuration...

Summary

As we have seen in this chapter, interacting with analytical reports is very similar to other web applications, so the user’s level of engagement and satisfaction can be measured in similar ways. Studies of use interfaces and web browsing suggest that a report that is generated in less than 5 seconds is ideal. They also suggest that reports completing in 10-second durations or higher should be considered carefully as this is the point of user frustration.

You should set performance targets and be prepared for outliers by measuring against baselines. Success may still require setting the right expectations by having different targets if you have highly complex reports.It is impotant to remember that each component of Power BI along with system resources can contribute to performance issues. Therefore, performance issues cannot be solved in isolation of just the report.

In the next chapter, we will focus on data conectivity with various storage modes of the semantic...

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Key benefits

  • Learn how to build performant data models and apply row-level security
  • Identify and fix performance issues in reports, DAX, and datasets using DAX Studio and VertiPaq Analyzer
  • Implement a formal process for performance management, from setting targets to monitoring and remediating issues
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

In a world dominated by data, organizations heavily rely on business intelligence tools like Power BI for deriving insights and informed decision-making. Yet, as data volumes grow and user demands increase, achieving optimal performance becomes challenging. Author Thomas LeBlanc, a seasoned Business Intelligence Architect with over 30 years of IT experience, draws on his extensive expertise to guide you in overcoming challenges related to slow performance, data modeling, query optimization, visualization design, deployment, administration, integration, compliance, and security. This book covers topics ranging from architecture and data modeling to Power BI premium features, DAX formulas, and collaboration, helping you to gain a deep understanding of the Power BI architecture, design efficient data models, create efficient queries, and improve visualization performance. You’ll also learn best practices to deploy and manage Power BI solutions, integrate with other tools and systems, ensure compliance and security, and collaborate with team members. By the end of the book, you’ll have developed your knowledge and skills to design, build, and maintain high-performing, scalable, and efficient Power BI solutions to drive unparalleled business success.

Who is this book for?

If you are a data analyst, BI developer, or a data professional who wants to maximize the performance of your Power BI solutions and understand how to build advanced analytics solutions, this book is for you. It assumes familiarity with the major components of Power BI and a beginner-level understanding of their purpose and use cases.

What you will learn

  • Master setting realistic performance targets and proactively addressing performance challenges
  • Understand the impact of architectural choices and configurations on performance optimization
  • Develop streamlined Power BI reports and enhance data transformations for optimal efficiency
  • Explore best practices for data modeling, mastering DAX, and handling large datasets effectively
  • Gain insight into the intricate workings of Power BI Premium for enhanced performance
  • Harness the potential of Azure services for extreme scale data processing
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Length: 346 pages
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Table of Contents

22 Chapters
Part 1: Architecture, Bottlenecks, and Performance Targets Chevron down icon Chevron up icon
Chapter 1: Setting Targets and Identifying Problem Areas Chevron down icon Chevron up icon
Chapter 2: Exploring Power BI Architecture and Configuration Chevron down icon Chevron up icon
Chapter 3: Learning the Tools for Performance Tuning Chevron down icon Chevron up icon
Part 2: Performance Analysis, Improvement, and Management Chevron down icon Chevron up icon
Chapter 4: Analyzing Logs and Metrics Chevron down icon Chevron up icon
Chapter 5: Optimization for Storage Modes Chevron down icon Chevron up icon
Chapter 6: Third-Party Utilities Chevron down icon Chevron up icon
Chapter 7: Performance Governance Framework Chevron down icon Chevron up icon
Part 3: Fetching, Transforming, and Visualizing Data Chevron down icon Chevron up icon
Chapter 8: Loading, Transforming, and Refreshing Data Chevron down icon Chevron up icon
Chapter 9: Report and Dashboard Design Chevron down icon Chevron up icon
Part 4: Data Models, Calculations, and Large Semantic Models Chevron down icon Chevron up icon
Chapter 10: Dimensional Modeling and Row Level Security Chevron down icon Chevron up icon
Chapter 11: Improving DAX Chevron down icon Chevron up icon
Chapter 12: High Scale Patterns Chevron down icon Chevron up icon
Part 5: Optimizing Capacities in Power BI Enterprises Chevron down icon Chevron up icon
Chapter 13: Working with Capacities Chevron down icon Chevron up icon
Chapter 14: Performance Needs for Fabric Artifacts Chevron down icon Chevron up icon
Chapter 15: Embedding in Web Apps Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Trying to performance tune an analytics solution, especially on the Power BI platform, can feel like navigating a maze for even the most seasoned developers and system administrators. Questions like, “Should I build an index in the source database?” “Is my DAX efficient enough?” “Should we use DirectQuery?” and “Can Power BI even handle this much data?”can come at you furiously. More often than not, the answer is a frustrating “it depends….”In their book, LeBlanc and Merchant do a great job of demystifying what clients of all sizes might mean when they ask for “better performance” from a report or dashboard. They break down the Power BI solution into its component tools and services, then lay out a roadmap for determining which layers might be the culprits behind performance issues.From tips on improving DAX to choosing alternative data loading patterns to recommending third-party tools for performance analysis, the authors provide both theoretical and practical methods for installations of all sizes. Unlike many other resources on performance tuning, LeBlanc and Merchant aren’t afraid to tackle the needs of clients using Power BI at scale. They dive into features and utilities available in Premium and other advanced SKUs, including alternative data load patterns that become essential when dealing with massive data volumes.I recommend it wholeheartedly.
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