Focus on this objective today: Transform and load the data: Create and transform columns. Explain it in your own words, apply it to one realistic scenario, and verify the details against the official source. Source context: Read in English Cleanse, transform, and load data into Unity Catalog Module 11 Units Feedback Intermediate Data Engineer Azure Databricks Data engineering requires transforming raw data into clean, well-structured formats ready for analysis. This module explores techniques for profiling data quality, selecting appropriate column types, resolving duplicates and null values, applying filtering and aggregation transformations, combining datasets with joins and set operators, reshaping data through pivoting and denormalization, and loading transformed data using append, overwrite, and merge strategies. Learning objectives By the end of this module, you'll be able to: Profile data using SQL commands and data profiling features to assess data quality Choose appropriate column data types to optimize storage and ensure data integrity Identify and resolve duplicate, missing, and null values in datasets Apply filtering, grouping, and aggregation operations to transform data Combine datasets using joins and set operators like UNION, INTERSECT, and EXCEPT Reshape data using denormalization, pivot, and unpivot techniques Load transformed data into Unity Catalog tables using INSERT, MERGE, and overwrite operations Add Prerequisites The following prerequisites should be completed: Basic understanding of Azure Databricks and Unity Catalog concepts Familiarity with SQL and Python programming Knowledge of data engineering concepts such as data quality and transformations Introduction min Profile data min Choose column data types min Resolve duplicates and nulls min Transform data with filters and aggregations min Transform data with joins and set operators min Transform data with denormalization and pivots min Load data with merge, insert, and append min Exercise - Cleanse, Transform, and Load Data into Unity Catalog min Module assessment min Summary min Take the module assessment Module Assessment Results Assess your understanding of this module.
Objective
Transform and load the data: Create and transform columns