Mastering Data Extraction: Techniques for Setting up an ETL Process

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Data extraction is the first step in the Extract, Transform, Load (ETL) process, involving retrieving data from diverse sources. This article delves into various data extraction techniques, providing detailed explanations, examples, and outputs to guide your ETL process setup.

1. SQL Queries

SQL queries are commonly used to extract data from relational databases. SELECT statements are crafted to fetch specific data or entire tables, based on filtering criteria or join conditions.


SELECT * FROM orders WHERE order_date >= '2023-01-01';

2. Change Data Capture (CDC)

CDC techniques capture only the changes made to the source data since the last extraction. This minimizes the amount of data transferred and processed during subsequent ETL runs, reducing latency and resource utilization.


Using database triggers or specialized CDC tools to capture INSERT, UPDATE, and DELETE operations on source tables.

3. APIs

Application Programming Interfaces (APIs) provide a structured way to access data from web services, databases, or cloud platforms. Data is retrieved in JSON, XML, or other formats via HTTP requests.


Fetching weather data from a weather API:

import requests

response = requests.get("")
weather_data = response.json()

4. File Processing

Flat files such as CSV, JSON, XML, or Excel spreadsheets are prevalent data sources. File processing involves reading and parsing these files to extract relevant data for ETL processing.


Parsing a CSV file using Python’s csv module:

import csv

with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    for row in reader:

5. Output Example

Below is an example of extracting data from a MySQL database using SQL queries in Python:

import mysql.connector

# Connect to the database
conn = mysql.connector.connect(

# Create a cursor
cursor = conn.cursor()

# Execute SQL query
cursor.execute("SELECT * FROM orders WHERE order_date >= '2023-01-01'")

# Fetch data
data = cursor.fetchall()

# Display output
for row in data:

# Close connection

Data extraction is a critical component of the ETL process, laying the foundation for subsequent data transformation and loading stages. By leveraging various data extraction techniques such as SQL queries, CDC, APIs, and file processing, organizations can efficiently gather data from diverse sources and fuel their data warehousing initiatives effectively.

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