In data warehousing, Slowly Changing Dimensions (SCDs) refer to the techniques used to handle changes to dimension data over time. Key-value stores, such as Apache Cassandra or Amazon DynamoDB, are popular choices for storing large-scale data. In this blog post, we will explore how to implement SCDs in key-value stores for efficient data management.

What are Slowly Changing Dimensions?

Slowly Changing Dimensions represent the historical changes to dimension data, such as customer attributes, product information, or employee details. Depending on the type of change, SCDs can be categorized into different classes:

1. Type 1 (SCD1): Overwrites the existing attribute with the new value, losing the historical data.
2. Type 2 (SCD2): Captures historical data by creating new records for each change with a surrogate key and effective and expiry dates.
3. Type 3 (SCD3): Stores both the current and previous values, enabling limited historical analysis.

Implementing SCDs in Key-Value Stores

Key-value stores are schema-less, making it challenging to implement SCDs as traditional relational databases do. However, we can leverage some strategies to achieve SCD functionality efficiently.

1. Type 1 (SCD1) Implementation:

For SCD1, where we only need to update the existing value, key-value stores work well. We can directly update the attribute using the primary key without any complexities.

# Example code using Python and Cassandra

from cassandra.cluster import Cluster

# Connect to the Cassandra cluster
cluster = Cluster(['localhost'])
session = cluster.connect('my_keyspace')

# Update customer age
session.execute("UPDATE customer SET age = 28 WHERE id = '1234'")

2. Type 2 (SCD2) Implementation:

To implement SCD2 in key-value stores, we need to generate a surrogate key and maintain effective and expiry dates for each record. We can create an additional table to store historical data, along with the primary key of the main table.

# Example code using Python and DynamoDB

import boto3

# Connect to the DynamoDB resource
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('my_table')

# Add new employee with SCD2
table.put_item(
    Item={
        'employee_id': '1234',
        'name': 'John Doe',
        'designation': 'Software Engineer',
        'hire_date': '2021-01-01',
        'effective_date': '2021-01-01',
        'expiry_date': '9999-12-31',
        'is_current': True
    }
)

# Update employee designation
table.put_item(
    Item={
        'employee_id': '1234',
        'name': 'John Doe',
        'designation': 'Senior Software Engineer',
        'hire_date': '2021-01-01',
        'effective_date': '2021-02-01',
        'expiry_date': '9999-12-31',
        'is_current': True
    }
)

# Set the expiry date for the previous designation
table.put_item(
    Item={
        'employee_id': '1234',
        'name': 'John Doe',
        'designation': 'Software Engineer',
        'hire_date': '2021-01-01',
        'effective_date': '2021-02-01',
        'expiry_date': '2021-02-28',
        'is_current': False
    }
)

3. Type 3 (SCD3) Implementation:

Key-value stores are not well-suited for SCD3 implementation due to their schema-less nature. However, we can partially achieve it by maintaining a separate column for previous values and updating it whenever a change occurs.

# Example code using Python and Cassandra

from cassandra.cluster import Cluster

# Connect to the Cassandra cluster
cluster = Cluster(['localhost'])
session = cluster.connect('my_keyspace')

# Update customer email and keep previous email
session.execute("UPDATE customer SET email_previous = email, email = 'new_email@example.com' WHERE id = '1234'")

Conclusion

While key-value stores are not ideal for implementing Slowly Changing Dimensions, we can apply certain strategies to handle different types of SCDs efficiently. By leveraging the flexibility and scalability of key-value stores, we can effectively manage changing dimension data in large-scale data warehousing environments.

#datawarehousing #SCD #implementation