ETL Basics

In the previous lesson, we learned about Data Warehouses and how they support analytics and reporting.

In this lesson, we focus on ETL — the process that moves data from source systems into a data warehouse.

ETL is the backbone of modern data platforms.

What is ETL?

ETL stands for:

• Extract
• Transform
• Load

It describes how raw data is collected, processed, and stored for analysis.

Why ETL is Important

Operational systems are not designed for analytics.

ETL ensures:

• Clean and consistent data
• Integrated data from multiple sources
• Historical tracking
• Reliable reporting

Without ETL, analytics systems fail.

Step 1: Extract

Extraction pulls data from source systems.

Common data sources:

• Relational databases (MySQL, PostgreSQL)
• APIs
• CSV / Excel files
• Logs and event streams

Extraction can be:

• Full extract
• Incremental extract

Full vs Incremental Extraction

Incremental extraction is preferred for large systems.

Step 2: Transform

Transformation cleans and reshapes data before loading.

Common transformations:

• Data cleaning (remove NULLs, fix formats)
• Data validation
• Data aggregation
• Data enrichment

Transformation Examples

-- Convert date format
SELECT DATE(order_date)
FROM orders;

-- Remove duplicates
SELECT DISTINCT customer_id
FROM sales;
  

Step 3: Load

Loading inserts transformed data into the target system.

Target systems include:

• Data warehouses
• Data marts
• Analytics databases

Loading strategies:

• Append-only loads
• Upserts (insert + update)
• Batch loads

ETL vs ELT

Modern systems often use ELT.

ETL Tools (Conceptual)

ETL is often implemented using tools such as:

• Apache Airflow
• Talend
• Informatica
• Cloud-native services

Regardless of tools, SQL plays a key role in transformations.

Role of SQL in ETL

SQL is used for:

• Data filtering
• Joins across sources
• Aggregations
• Data validation

Strong SQL skills are essential for ETL engineers.

ETL Best Practices

• Validate data at each step
• Log errors and failures
• Design for incremental loads
• Make pipelines idempotent

Common Beginner Mistakes

• Using full loads unnecessarily
• Skipping data validation
• Hardcoding transformations
• Not monitoring ETL jobs

Real-World ETL Use Cases

• Daily sales reporting
• Customer analytics
• Financial dashboards
• Machine learning pipelines

What’s Next?

In the next lesson, we will work on Advanced SQL Projects, bringing together advanced queries, performance tuning, and analytics concepts.