Author(s): Ganesh Bajaj

Originally published on Towards AI.

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SQL is a powerful language for analyzing and manipulating big datasets, making it an essential skill for data analysts and data scientists. Even though tools like PySpark offer parallel processing capabilities in environments like Databricks, SQL remains highly effective due to its performance optimizations and simplicity.

This article covers why SQL is preferred for working with large datasets in SQL databases and provides best practices for handling multiple tables, especially when performing joins and data quality checks.

If you are a beginner with SQL for data analysis, this will be highly informative article.

Using SQL for data analysis in SQL-based databases, such as the Databricks catalog, comes with several advantages:

Declarative Language and Optimizations: SQL is a declarative language, allowing users to specify what data they want rather than how to retrieve it. SQL engines optimize query execution, which is beneficial for handling large datasets efficiently without needing extensive code optimization.Simplicity and Readability: SQL queries, especially for common operations like joins, aggregations, and filters, are concise and often more readable than equivalent PySpark code.Pushdown Capabilities: Databricks SQL and Spark SQL allow operations to be “pushed down” to… Read the full blog for free on Medium.

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