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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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.
When managing Power BI content like reports and datasets, knowing which ones to trust is critical. Power BI’s promotion and certification features help you highlight and share reliable content within your workspace. But what do these features mean, and how are they different? In this article, we’ll explain promotion vs certification in Power BI, introduce the concept of master data, and show you when to use each.
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Parallel sets are a visually striking way to represent flows and relationships between categories, offering deeper insights into complex data distributions. In this blog, we’ll guide you step by step on how to create a parallel set visualization in Tableau using the Sample Superstore dataset.
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Table calculations in Tableau are powerful tools that allow you to perform computations directly on the aggregated results in your visualization. Unlike calculated fields (which operate at the data source level), table calculations depend on the structure of your table or pane, making them ideal for dynamic, context-aware metrics. In this guide, we’ll break down compute using options, advanced settings like addressing and partitioning, and demonstrate practical examples using Tableau’s Superstore dataset.
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