Chapter 1: 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 a complete 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 due to the data being stale.
The author of this book has experienced the effects of poor report performance firsthand. In one project, a large utility company underwent a large migration from one reporting platform to another, from a different vendor. Even though the new platform was technically and functionally superior, the developers tried to copy the old reporting functionality across exactly. This led to poor design choices and very slow report performance. Millions of dollars in licensing and consulting fees were spent, yet most users refused to adopt the new system because it slowed them down so much. While it is extreme, this example demonstrates the potential ramifications when you do not build good performance into an analytical solution.
In this chapter, you will begin your journey to achieving good and consistent performance in Microsoft Power BI. To introduce the full scope of performance management, we will describe a Power BI solution as a stream of data from multiple sources being consolidated and presented to data analysts and information workers. We look at how data can be stored in Power BI and the different paths it can take before reaching a user. Many of the initial architectural design choices made in the early stages of the solution are very difficult and costly to change later. Hence, it is important to have a solid grasp of the implications of these choices and use a data-driven approach to help us decide what is best right at the start.
An area of performance management that is easily overlooked is that of setting performance targets. How do you know whether the experience you are delivering is great, merely acceptable, or poor? We will begin by exploring this theoretical area first to define our goals before diving into technical concepts.
This chapter is broken into the following sections:
- Defining good performance
- Considering areas that can slow you down
- Which choices affect performance?
