Six common mistakes made by Power BI developers

Now, you might be wondering, what’s the big deal with star schemas, and why should you care about them in Power BI development?

A star schema is a fundamental concept in the world of data modeling, and it’s especially crucial in the Power BI world. At its core, a star schema organizes your data in a way that simplifies relationships, optimizes performance, and makes your life as a Power BI developer a whole lot easier. Of course there are situations where “flat table” or any other technique become handy but in most cases star schema will be the best option.

What are the common problems when the model is not in star schema?

• Complexity : When you avoid the star schema and choose a more complicated data structure, things can become messy fast. Your connections get mixed up, and your Power BI model becomes like a jumbled mess that’s tough to work with.
• Performance: Power BI is all about speed and efficiency. Without a star schema, your reports may become sluggish, leaving your end-users frustrated with slow-loading visuals and dashboards.
• Maintenance: Ever tried fixing a complex data model with multiple layers of relationships? Only one thing come to my mind when I see something like this – long and painful process of decoding it. A star schema simplifies maintenance, making it easier to add new data or make changes without causing headaches.

So how to start implementing star schema?

Let’s take another common pitfall that’s easy to stumble into – overusing many-to-many relationships and bi-directional filtering. These are powerful features, no doubt, but they should be used with caution.

Now, don’t get me wrong, many-to-many relationships and bi-directional filtering can be lifesavers when used appropriately. However, the trouble begins when you overuse them without a clear reason. Here’s why it can be problematic:

• Complexity Overload: Too many many-to-many relationships and bi-directional filters can quickly turn your data model into a tangled web. It becomes challenging to understand the logic behind your model and troubleshoot issues.
• Performance Bottlenecks: Power BI’s performance can take a hit when these features are overused. The engine has to work harder to resolve complex relationships, potentially leading to slower report rendering and longer query response times.
• Maintenance Nightmare: As your model becomes more complicated, maintaining it becomes a Herculean task. Making changes or adding new features can result in unintended consequences, and debugging can turn into a marathon.

This point is not only for Power BI Developers but also for other personas like architects and security engineers. Data security is not a luxury – it’s a necessity! Ignoring it can lead us to data breaches, privacy problems and loss of trust. It is not something that has additional value – it should be treated as an obligatory point when developing a report.

What you can do?

• Row-Level Security: Implement row-level security to ensure that users can only access the data they are authorized to see. This is crucial when dealing with sensitive data.
• Data Encryption: Ensure that data transmitted between Power BI and data sources is encrypted. In most cases it is but please be sure.
• Role-Based Access: Define roles and assign permissions based on users’ roles and responsibilities. Not everyone needs access to every piece of data.
• Secure Data Sources: Pay attention to the security of your data sources. Databases and other data repositories should have proper access controls in place.
• Regular Audits: Conduct regular security audits to identify vulnerabilities and address them when needed.
• User Training: Educate your users about data security best practices, such as the importance of not sharing sensitive information.
• Data Masking: Consider data masking to hide sensitive information in reports while still providing valuable insights.
• Privacy Levels: Consider using proper privacy levels when you are dealing with multiple data sources and query folding to avoid problems.

This issue arises when we ignore critical factors like query folding and pulling in more data than necessary. Mishandling can lead to some significant problems:

• Performance: Ignoring query folding and bringing in excessive data can bog down your reports, causing sluggish loading times and frustrating your users but also block your data source for too much time.
• Resource Drain: Power BI can be a resource-intensive application. Inefficient data loading can consume unnecessary resources, slowing down your entire platform. Even if your single report has acceptable refresh process you can waste
• Data Bottlenecks: Failing to leverage query folding and load only the data you need can strain your data sources, potentially causing bottlenecks and impacting other processes.

Now, let’s discuss how to avoid above problems and keep your projects running smoothly:

• Query Folding: Take advantage of query folding whenever possible. Query folding means pushing as much of the data transformation work back to the data source rather than processing it within Power BI. This can significantly improve performance.
• Data Pruning: Don’t be a data hoarder. Only load the data you need for your reports and dashboards. Unnecessary data not only slows things down but also makes your projects harder to manage.
• Data Sampling: When dealing with large datasets, consider using data sampling for development and testing. This involves working with a subset of your data during development and testing, which can speed up your workflow and not impact your capacity.
• Incremental Loading: If your data source supports it, implement incremental loading. This allows you to fetch only the new or changed data since your last update, reducing the volume of data transferred.
• Data Source Optimization: Ensure your data source is optimized for queries. This might involve indexing, partitioning, or other performance-enhancing techniques specific to your data platform.

Power BI comes packed with an array of powerful tools and capabilities, but sometimes, we find ourselves reinventing the wheel when there’s no need to. How many times have I seen overcomplicated use of bookmarks or custom visuals that end up with results that can be easily achievable with much simpler built-in features? Many times and I am sure you have similar experiences.

To avoid such situations I see a few things that we should follow:

• Feature Exploration: Before embarking on custom solutions, thoroughly explore the existing features and capabilities of Power BI. You might be surprised by how much it can do out of the box.
• User Training: Ensure that your team and end-users are familiar with Power BI’s capabilities. Sometimes, the solution to a problem lies in knowing how to use the existing tools effectively.
• Customization within Limits: While built-in features are fantastic, there may be cases where a little customization is needed. In such instances, strive to tweak existing features rather than reinventing them entirely.
• Stay Updated: Keep abreast of Power BI updates and new releases. These updates often bring enhancements and additional functionality that can address your specific needs.
• Community Resources: Tap into the Power BI community for guidance and solutions. You’re likely not the only one facing a particular challenge, and someone might have already found a clever workaround using existing features.

I don’t want to say that creativity is wrong, but you should always prefer simplicity for your own sake.