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A calculated column is similar to a spreadsheet column. Imagine you have sales data (like product, quantity, and price). In this example, you could add a calculated column that adds up the total sales for each row. Now, a measure is a special formula that changes displayed data depending on applied filters.  Referring back to the above example, you could request sales for a particular month or quarter. In short, measures offer flexibility that calculated columns do not.  

First, I would analyze the data set for unnecessary columns and rows and delete them. Then, I would favor using measures over calculated columns, as the former is a more efficient use of bandwidth. I would also use query folding to get the SQL database to handle filtering tasks instead of forcing Power BI to do it. Lastly, I would partition your data set so only the most recent data gets refreshed (rather than the entire database.)

My dashboard creation process begins with the end users. I don’t guess – I conduct thorough research through interviews and ongoing contact. Then, I begin the data integration process, paying particular attention to make sure everything (a) makes sense and (b) is accurate. Next, I would engage in data modeling. As I do, I run data analysis to see if it performs as expected. After that, I build the data analytics dashboard, then beta test the application with selected colleagues, and iterate based on feedback.

I try to keep things simple. I use star schema, as it only uses a central fact table and a few dimension tables. This way, queries process quickly. Also, I only include relationships relevant to the people using the model. For fellow Power BI developers, I give columns clear names, so they can easily work with my database. And I delete unnecessary columns and always look to simplify formulas. Over time, doing this will improve Power BI  data model performance.

When working with large datasets, I optimize performance by removing unnecessary columns and rows during the data import process. I use aggregation techniques to summarize data at higher levels rather than working with raw, detailed data. Utilizing DirectQuery instead of importing large datasets also helps with real-time data analytics. I also optimize DAX calculations by using efficient formulas and favoring measures over calculated columns to reduce memory usage and improve speed, which is critical for smooth data visualization.

Row-Level Security (RLS) restricts data access for specific users. To implement it, I define roles in Power BI Desktop by creating DAX filters that control the data visible to each role. For example, if managers should only view sales data for their specific regions, I configure RLS rules based on region data. After publishing the report to Power BI Service, I assign users to their respective roles and test to confirm they only access the intended data. This setup is vital for maintaining business intelligence security while still providing actionable insights.

When troubleshooting Power BI reports or dashboards, I first review the DAX formulas for potential errors or inefficiencies that could affect performance or results. I then check the data source connections to confirm they’re functioning properly. If the issue is related to data accuracy, I investigate the data transformations and analyze data flows from the source. For visual problems, I review the layout and visual configurations to make sure they reflect the intended business intelligence output.

For complex calculations, I break down the problem into smaller, manageable parts. I use variables in DAX to store intermediate results, which simplifies complex expressions and boosts performance. Additionally, I leverage functions like CALCULATE and FILTER to adjust the data context, making it easier to apply specific filters or conditions. This method helps maintain clarity and improves the accuracy of data analytics in the final reports.

For data integration, I connect Power BI to various sources like SQL databases, Excel files, and cloud services. Using Power Query, I clean, transform, and merge data from these sources before loading it into the Power BI model. I standardize formats, resolve discrepancies, and merge tables when needed to create a unified view of the data. This approach supports seamless data modeling and empowers users to analyze data from multiple systems within a single business intelligence dashboard.

In Power BI Service, I publish reports and dashboards for secure sharing with stakeholders. I use workspaces to organize content and assign access rights based on user roles. I also schedule data refreshes to keep reports up-to-date automatically. For collaboration, I share reports with teams, enable comments for feedback, and create subscriptions for users to receive updated reports via email. This approach supports project management, ensuring teams stay aligned on the latest data insights and business intelligence.