Extract, Transform, and Load (ETL) Changing Fields

Description

This class discusses Extract, Transform, and Load (ETL) as a process for obtaining data and preparing it for analysis. ETL is important because accounting data frequently need to be collected, cleaned, or reorganized before they can be used to answer a question. This class focuses on using formulas to change fields.

Motivation

How do we turn raw data fields into usable data fields?

Review

Prior classes considered problems that can affect the quality of accounting data and introduced ETL concepts and joins. This class introduces a systematic process for changing raw data fields we receive toward a dataset that is ready for analysis containing the fields we require for analysis.

Preparation

• Review the introductory material on data quality and ETL processes.
• Optional, read the case Analytics mindset case studies ETL Case2 for background on changing data fields.

Class plan

• We will begin with a brief review of the conceptual and practical joins material from the prior class (using PollEverywhere).
• We will use PowerBI in the remote labs to undertake some data transformations that involve working with string fields (i.e. text data).
• We will also use PowerBI to revisit joins to reinforce the material from the prior class.

Materials

• Case

  EYARC Transform Fields Case

  An introductory ETL case on focusing on extracting and changing information in data fields.

  View Online: EYARC Transform Fields CasePDF: EYARC Transform Fields CaseWord: EYARC Transform Fields Case

• Slides

  Class 03 Slides

  View slides: Class 03 Slides

• Slides

  Class 04 Slides

  View slides: Class 04 Slides

Concepts

• Primary keys

Data

Use git pull to extract any require data for this class.