》》》JagritiSachdeva

CHAPTER 1

Data Extraction: The Foundation of ETL

Data extraction is the first and one of the most critical steps in the Extract/Transform/Load (ETL) process. It involves retrieving data from various sources—structured or unstructured—so that it can be transformed and loaded into a centralized system for analysis and decision-making. When done correctly, data extraction enables organizations to gain valuable insights, enhance operational efficiency, and drive strategic decisions.

Why Is Data Extraction Important?

In today’s digital world, organizations generate vast amounts of data from multiple sources, including:

• Databases (SQL, NoSQL)
• APIs & Web Services
• Logs & IoT Devices
• Spreadsheets & Documents
• Social Media & Web Scraping

Key Techniques for Data Extraction

Organizations can use different extraction techniques based on their requirements:

• Full Extraction: Extracts all available data, typically used for initial loads.
• Incremental Extraction: Captures only the newly added or modified data since the last extraction.
• API-Based Extraction: Uses web APIs to pull data from cloud-based applications and services.
• Web Scraping: Gathers data from websites when APIs are unavailable.

Techniques for Data Extraction

Data extraction can be categorized into four primary techniques. The choice of technique depends on the type and structure of the data source. These techniques ensure efficient retrieval, organization, and processing of data for further analysis.

1. Association