SQL window functions are one of the most powerful tools for performing calculations across a defined range of rows while maintaining access to individual row-level details. Unlike aggregate functions that collapse rows into a single value, window functions allow you to perform calculations over a subset of rows (a window) while preserving the original row structure.
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Data processing systems have come a long way, transforming to meet the ever-growing needs of modern businesses. In this blog, we’ll dive into three major systems that have shaped the landscape: OLTP (Online Transaction Processing), OLAP (Online Analytical Processing), and the cutting-edge HTAP (Hybrid Transaction/Analytical Processing).
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If you work with data, SQL functions are your toolkit for unlocking powerful insights. Whether you’re crafting detailed reports or uncovering business-changing analytics, SQL is the backbone for data doers in every industry. This guide explores 27 SQL functions that every data analyst, data engineer, or analytics enthusiast should know. These functions are grouped into five categories for easier understanding and practical use.
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Geographic data is often stored in a Degree-Minute-Second (DMS) format, making it a bit tricky to convert into the standard decimal degrees (DD) required for Tableau’s mapping functions. In this post, I’ll walk you through how to transform DMS coordinates to decimal degrees directly in Tableau, step-by-step, so you can plot your geographic data without headaches.
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In this post, I’ll explain how to add the current date and time to an existing PySpark DataFrame in a Fabric Notebook. This is particularly helpful when inserting data into a Fabric Lakehouse table, as it allows you to track when each record was added. I’ll walk through an example using a DataFrame to first load some sample data and show how to append a new column with the current timestamp to capture the insertion time.
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Data warehousing and business intelligence are crucial for modern enterprises, enabling them to make informed decisions based on historical data. One key aspect of data warehousing is handling changing data over time, especially when tracking historical changes in dimensions. This is where Slowly Changing Dimensions (SCD) come into play. In this blog post, we’ll delve into what SCDs are, the different types of SCDs, and how to implement them effectively.
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