Welcome to data transformation patterns! Remember our ETL workflows from before? Now let's master the specific patterns that solve real-world data challenges.

These patterns are like recipes - reusable solutions for common data problems you'll encounter in every data processing job.

Engagement Message

Name one data quality issue you've encountered in real datasets?

Let's start with deduplication - removing duplicate records. Imagine customer data where "John Smith" appears three times with slightly different spellings or addresses.

Simple deduplication removes exact duplicates, but fuzzy deduplication handles similar records that should be merged.

Engagement Message

Give one reason exact matching may miss duplicates humans would spot?

Here's a common deduplication strategy: identify key fields that should be unique (like email addresses), then keep only the most recent or complete record for each key.

For example, if two customer records have the same email but different phone numbers, keep the one with the phone number filled in.

Engagement Message

Name another field that could act as a deduplication key besides email?

Now let's explore slowly changing dimensions (SCDs) - handling how data changes over time. Think about a customer's address - it changes, but you might want to keep historical records.

SCD Type 1 overwrites old values, Type 2 keeps historical versions, and Type 3 stores the current value and the previous value in separate columns.

Engagement Message

If you're tracking customer addresses for shipping analytics, which type would be most useful?

Surrogate keys are artificial identifiers you create instead of using natural keys. Instead of using "John Smith" as a customer identifier, you assign "CUST_001".

This protects against changes in natural identifiers and ensures uniqueness across your entire data warehouse.

Engagement Message

What problems might arise if you use customer names as primary identifiers?

Business logic transformations apply your organization's specific rules to data. This might include calculating customer lifetime value, categorizing transactions, or applying pricing rules.

These transformations encode domain knowledge and business rules directly into your data processing pipeline.

Engagement Message

Name a business rule that should be automated in data processing?

The key is choosing the right pattern for each situation. Deduplication for data quality, slowly changing dimensions for historical tracking, surrogate keys for stability.

Most real-world pipelines combine multiple patterns to handle different aspects of data transformation.

Engagement Message

Which transformation pattern would you use for tracking product price changes over time?

Type

Swipe Left or Right

Practice Question

Let's practice identifying the right transformation pattern! Match each scenario with the best transformation approach:

Labels

• Left Label: Deduplication
• Right Label: Dim Changes

Left Label Items

• Customer records with same email but different phone numbers
• Product catalog with identical items from different sources
• Transaction logs with duplicate entries from system errors
• User profiles imported from multiple systems with overlapping data

Right Label Items

• Employee job title changes over time
• Customer address updates for shipping history
• Product category reassignments for trend analysis
• Store location changes for regional reporting