Hackforge Academy

Explanation

ETL stands for Extract, Transform, and Load. It is a process used to collect data from multiple sources, clean and transform it into a usable format, and load it into a database or data warehouse for reporting and analytics.

Code:

Source Files/DBs -> Extract -> 
Transform -> Load -> 
PostgreSQL/Data Warehouse

Explanation

Extract is the first step where data is gathered from sources such as CSV files, Excel sheets, APIs, databases, or cloud storage.

Code:

customers_df = spark.read.csv
('customers.csv', header=True)

Explanation

Transform is the process of cleaning, validating, filtering, aggregating, and enriching the extracted data.

Code:

clean_df = customers_df.filter(
customers_df.age > 18)

Explanation

Load is the final step where the transformed data is stored in a target system such as PostgreSQL, MySQL, Snowflake, or a data warehouse.

Code:

clean_df.write.jdbc(postgres_url, 
'customers', mode='append')

Explanation

In a BFSI project, ETL can extract customer and transaction data, calculate risk metrics, and load curated data into PostgreSQL for Power BI dashboards.

Code:

Transactions + Customers -> ETL -> 
Curated Tables -> PostgreSQL -> Power BI