Apprendre Data Warehousing and ETL | What is Business Intelligence?

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Prérequis

Introduction to Python

To understand how organizations turn raw data into actionable insights, you need to know the key concepts of Data Warehousing and ETL. A Data Warehouse is a centralized repository designed for storing large volumes of structured data from multiple sources, optimized for querying and analysis rather than transaction processing. The ETL (Extract, Transform, Load) — is a fundamental method for moving data from various sources into the data warehouse, ensuring it is clean, consistent, and ready for business intelligence tasks.

There are two common approaches for handling data movement and transformation: ETL and ELT. The table below compares these approaches, defines their roles, and highlights their strengths and weaknesses.


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# Simple ETL-like process using Python lists and dictionaries

# Step 1: Extract - Getting data from a "source"
raw_data = [
    {"id": 1, "name": "Alice", "sales": "1000"},
    {"id": 2, "name": "Bob", "sales": "1500"},
    {"id": 3, "name": "Charlie", "sales": "not available"},
]

# Step 2: Transform - Cleaning and converting data
transformed_data = []
for record in raw_data:
    # Convert 'sales' to integer, set to 0 if not a valid number
    try:
        sales = int(record["sales"])
    except ValueError:
        sales = 0
    transformed_record = {
        "id": record["id"],
        "name": record["name"].title(),
        "sales": sales
    }
    transformed_data.append(transformed_record)

# Step 3: Load - Storing the clean data in a new "data warehouse" list
data_warehouse = transformed_data

# Printing the final data warehouse content
for row in data_warehouse:
    print(row)

This code simulates a basic ETL process. First, the extract step gathers raw sales data from a source, which may include inconsistencies like non-numeric sales values. Next, the transform step cleans the data: it converts the sales field to an integer, setting it to 0 if the value is invalid, and formats the name properly. Finally, the load step places the cleaned data into a new list, representing the data warehouse, ready for analysis or reporting. This mirrors how real-world ETL tools prepare data for business intelligence.

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