Event-driven architecture patterns in DynamoDB - Mini Project: Build & Apply

Event-driven Architecture with DynamoDB Streams

📖 Scenario: You are building a simple inventory system that tracks product stock levels. When a product's stock changes, you want to capture this event and store it separately for auditing and analytics.

🎯 Goal: Create a DynamoDB table to store products, enable DynamoDB Streams to capture changes, and write a simple event handler function that processes these change events.

📋 What You'll Learn

Create a DynamoDB table named Products with ProductID as the primary key.

Enable DynamoDB Streams on the Products table to capture item modifications.

Create a variable called stream_arn to hold the stream ARN of the Products table.

Write a function called process_event that takes a DynamoDB stream event record and extracts the ProductID and Stock values.

Add a final step to simulate processing a sample event record.

💡 Why This Matters

🌍 Real World

Event-driven architectures are common in modern cloud applications to react to data changes in real time, such as updating dashboards or triggering workflows.

💼 Career

Understanding how to use DynamoDB Streams and process events is valuable for backend developers and cloud engineers working with AWS services.

1

Create the DynamoDB Products table

Create a variable called products_table that represents a DynamoDB table named Products with ProductID as the primary key.

DynamoDB

# Your code here to create products_table with ProductID as primary key

Need a hint?

Define products_table as a dictionary with keys TableName, KeySchema, and AttributeDefinitions.

2

Enable DynamoDB Streams on the Products table

Add a key StreamSpecification to products_table with StreamEnabled set to True and StreamViewType set to NEW_AND_OLD_IMAGES. Then create a variable called stream_arn and set it to the string 'arn:aws:dynamodb:region:account-id:table/Products/stream/2024-06-01T00:00:00.000'.

DynamoDB

products_table['StreamSpecification'] = {
    'StreamEnabled': True,
    'StreamViewType': 'NEW_AND_OLD_IMAGES'
}
# Your code here to create stream_arn variable

Need a hint?

Update StreamSpecification inside products_table and create stream_arn as a string variable.

3

Write the event processing function

Write a function called process_event that takes one parameter record. Inside the function, extract ProductID and Stock from record['dynamodb']['NewImage'] assuming they are strings and numbers respectively, and return them as a tuple (product_id, stock).

DynamoDB

def process_event(record):
    # Extract ProductID and Stock from record
    # Your code here

Need a hint?

Use dictionary keys to access NewImage data and convert stock to integer.

4

Simulate processing a sample event record

Create a variable called sample_record that mimics a DynamoDB stream event record with ProductID set to 'P123' and Stock set to 50. Then call process_event(sample_record) and assign the result to a variable called result.

DynamoDB

sample_record = {
    # Your code here to define sample_record
}

result = process_event(sample_record)  # Your code here

Need a hint?

Define sample_record as a nested dictionary matching DynamoDB stream format and call process_event.