When working with databases, business analysts often come across two common objects: stored procedures and views. Both play a key role in querying and managing data, but they serve different purposes. Understanding their differences helps analysts write better queries, collaborate effectively with engineers, and choose the right tool for their reporting needs.
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If you’ve worked with data at any level—business analyst, marketing manager, or even startup founder—you’ve probably heard the terms Business Intelligence (BI) and Data Science thrown around. Sometimes they seem interchangeable. Other times, people treat them like polar opposites.
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CTE (Common Table Expression) is a temporary result set defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement.
Imagine you launch a new checkout banner promising free shipping. It feels good, it looks good—but does it actually boost purchases?
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When you work in business intelligence, you inevitably deal with files—lots of them. Sometimes you’ll get a flood of daily CSV exports from your data warehouse. Other times, you’ll inherit a single monster file so large that Excel waves the white flag before it even opens.
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When you’re building a data warehouse, the way you model your data can make the difference between fast, intuitive analytics and a never-ending maze of joins. Two of the most common data modeling approaches are Star Schema and Snowflake Schema. Both serve the same purpose—structuring your data to support reporting and analysis—but they differ in design, performance, and usability.
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If you’re learning data analysis with Python, mastering the pandas DataFrame is essential. A DataFrame is a powerful, table-like data structure that lets you load, explore, clean, and analyze data quickly.
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In Business Intelligence (BI), SQL is used to extract and manipulate data from databases, while Python adds flexibility for data processing, visualization, and automation. Combining both enables you to build powerful, automated BI pipelines and dashboards.
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Stacked bar charts are often used to display the breakdown of data within categories, but they have a big drawback: once you go beyond the first layer, comparing the rest becomes visually difficult. That’s where grouped bar charts step in.
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A Pareto chart is a powerful visualization that combines both bar and line charts to identify the most significant factors in a dataset. Named after the Pareto principle (also known as the 80/20 rule), it helps users focus on the causes that generate the majority of results. In this article, we’ll walk through how to build a Pareto chart in Power BI using the Superstore dataset, a common sample dataset used in data visualization training.
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