Introduction

Raw data rarely arrives in a format ready for analysis. Missing values, duplicate records, inconsistent data types, and poorly structured datasets are common challenges that data engineers must address before data can deliver business value. Without proper cleansing and transformation, analytics results become unreliable and downstream processes fail. Unity Catalog in Azure Databricks provides the governance foundation for managing transformed data assets throughout this process.

Azure Databricks offers a comprehensive toolkit for data quality and transformation work. Data profiling generates summary statistics that reveal issues like null percentages, unexpected value distributions, and data drift. Type selection ensures columns use appropriate data types for storage efficiency and query performance. Duplicate and null handling techniques let you identify and resolve common data quality problems using SQL or PySpark. Transformation operations including filtering, grouping, aggregation, joins, and set operators reshape data to meet analytical requirements.

Beyond basic transformations, you'll work with advanced reshaping techniques. Denormalization flattens related tables for faster query performance. Pivoting rotates row values into columns for cross-tabular analysis, while unpivoting reverses this process for normalized data structures. Finally, loading strategies like append, overwrite, and merge ensure transformed data lands correctly in target tables—whether you're adding new records, replacing existing data, or synchronizing changes through upsert operations.

By the end of this module, you'll understand how to assess data quality through profiling, apply cleansing techniques to resolve common issues, transform data using SQL and PySpark operations, and load the results into Unity Catalog tables with the appropriate strategy for each scenario.