Welcome to batch processing! Previously we learned how Spark coordinates work across clusters. Now let's see how to use that power for real data transformation.

Batch processing means working with large chunks of data all at once, rather than processing records one by one.

Engagement Message

What is one scenario where batch processing is a better fit than real-time (record-by-record) processing?

The most common pattern in batch processing is ETL - Extract, Transform, Load. Think of it like renovating a house: you gather materials, modify them, then put them in place.

ETL workflows are the backbone of most data processing pipelines in business environments.

Engagement Message

Can you think of a real-world example where ETL might be useful?

The Extract phase is like gathering ingredients for cooking. You collect data from various sources - databases, files, APIs, or web services.

In Spark, this means reading data into DataFrames (a table-like data structure) from different formats like CSV, JSON, or Parquet files.

Engagement Message

What challenges might you face when extracting data from multiple sources?

The Transform phase is where the magic happens! This is like actually cooking - you clean, filter, aggregate, and reshape your data.

Common transformations include removing duplicates, calculating averages, joining datasets, and creating new calculated columns.

Engagement Message

What's one transformation you might need to do on customer purchase data?

The Load phase is like serving the finished meal. You take your transformed data and store it somewhere useful - maybe a data warehouse or analytics database.

The goal is making data ready for analysts, dashboards, or machine learning models to consume.

Engagement Message

Why do you think the Load phase is just as important as Transform?

Spark DataFrames make ETL workflows much easier. They provide a table-like structure with built-in functions for common transformations.

You can use either DataFrame methods or SQL syntax - both work on the same underlying data structure.

Engagement Message

For someone just starting with Spark, which interface—DataFrame methods or SQL—do you expect would feel more intuitive?

Type

Sort Into Boxes

Practice Question

Let's practice identifying ETL phases! Sort these activities into the correct ETL phase:

Labels

• First Box Label: Extract
• Second Box Label: Transform

First Box Items

• Read CSV
• Import JSON

Second Box Items

• Join tables
• Filter rows
• Calculate sum