APX AI
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After extracting data from its origin, the next task is to prepare it for its destination and intended use. Raw data is frequently inconsistent, incomplete, or structured improperly for analysis or storage in a target system. This intermediate step, transformation, addresses these issues.
This chapter concentrates on the 'Transform' stage within the ETL framework. You will learn common operations applied to refine raw data. We will cover techniques for data cleaning, including handling missing values and correcting inaccuracies. You'll also learn about applying validation rules, standardizing formats, enriching data by adding calculated or lookup fields, structuring data through operations like joins or splits, and the basics of data aggregation.
The goal is to reshape the extracted data into a usable, consistent format ready for the subsequent loading phase.
3.1 Why Data Transformation is Necessary
3.2 Data Cleaning: Handling Missing Values
3.3 Data Cleaning: Correcting Errors
3.4 Data Validation Rules
3.5 Data Formatting and Standardization
3.6 Data Enrichment: Adding Information
3.7 Data Structuring: Joining and Splitting Data
3.8 Introduction to Data Aggregation
3.9 Practice: Applying Simple Transformations