SQL is still the most commonly used query language. Many people use it for analysis tasks, such as searching for a specific user in a database, exporting rows to Excel, and grouping categories, among others. However, in data engineering, SQL is not just a tool for data analysis; it plays a key role in building […]
Many people jump straight into building models, eager to extract insights or maximize accuracy. However, without a proper data pipeline to clean, structure, and process your data, your model will either fail or produce results that look good but are misleading. The Messy Truth About Real-World Data In an ideal world, data would be clean, […]
Data doesn’t just magically become useful. Whether you’re building dashboards, feeding machine learning models, or just trying to get a cleaner look at last quarter’s sales, you need data that’s structured, clean, and actually means something. And that’s where transformation comes in, particularly through ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. But […]
If you’ve worked with data, especially in Python, Spark, or R, you’ve probably come across the terms Dataset and DataFrame. They sound similar, but they’re actually a bit different depending on the tool or framework you’re using. DataFrame A DataFrame is a two-dimensional tabular data structure that resembles an Excel sheet or a database table, […]