When working with Power BI, mastering DAX (Data Analysis Expressions) is essential for unlocking advanced analytics and building dynamic, interactive reports. DAX is the formula language used to perform calculations, aggregations, and analysis within Power BI. Whether you’re a beginner or an advanced user, understanding the top DAX functions for Power BI is critical to transforming your data and gaining valuable insights.
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If you’ve ever stared at a blank Tableau dashboard wondering how to move beyond bar and line charts — you’re not alone.
Sure, those basics serve a purpose. But data storytelling is about more than ticking boxes. It’s about helping your audience see the story behind the numbers. That’s why I started building a personal catalog of unique, visually engaging Tableau chart types — and now I’m sharing them with you.
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Data visualization is a powerful tool for conveying insights, but the effectiveness of a visualization largely depends on how well the chosen chart represents the underlying data. As a data analyst, selecting the appropriate chart type is critical to ensuring your message is communicated clearly and effectively. In this guide, we’ll explore the most common chart types, their strengths and weaknesses, and when to use each one.
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Working with dates in Tableau is essential for trend analysis, time-series visualizations, and dynamic dashboards. Tableau provides a rich set of date functions that help you parse, manipulate, and calculate date values with ease.
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Radar charts, also called spider charts, are great for comparing multiple variables across categories. While Tableau doesn’t natively support radar charts, you can still build one using calculated fields and some data reshaping.
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In Power BI, effectively ranking data is crucial for insightful analysis. Two primary DAX functions facilitate this: TOPN and RANKX. While both serve to rank data, they operate differently and are suited for distinct scenarios.
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In the world of data processing, SQL is the lingua franca—but not all SQLs are created equal. If you’ve worked with big data tools like Apache Hive, you’ve probably noticed that Hive SQL isn’t exactly the same as traditional SQL used in relational databases like MySQL, PostgreSQL, or SQL Server.
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When working with datasets containing UTC timestamps, especially across multiple U.S. locations, it’s important to convert UTC time to the correct local time based on time zone and daylight saving time (DST) rules.
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TL;DR:
If you’re using a LEFT JOIN but filtering on the right table in the WHERE clause, you might unintentionally turn it into an INNER JOIN. Here’s why that happens, how to fix it, and how different databases handle it.
As data grows larger and more complex, optimizing for performance and scalability becomes essential. Partitioning is one of the most powerful strategies for managing big datasets efficiently. If you’ve come across a column like p_date in an SQL query, it often signals the use of table partitioning. But what does that mean, and how is it different from traditional databases?