What is a Slowly Changing Dimension, and the Logic in Implementation

What is a Slowly Changing Dimension Its Type, and the Logic in Implementation

In business, one of the most important resources you need to manage is time; you have only X number of hours in a day in which you have to complete your tasks.

This is especially true when you are working in a service industry where you have to complete all your routine tasks plus any additional chores that may arise from some customers requiring impromptu services. But time is not just something that affects you, it also affects your data.

When working with your data in a data warehouse, you might assume that most dimensions of your database stay the same. Sometimes, what appears to be a static dimension is actually a slowly changing dimension or SCD.

Table of Contents

  1. OLAP vs OLTP: the Difference
  2. What is OLAP (Online Analytical Processing)?
  3. What is OLTP (Online Transaction Processing)?
  4. Typical Star Schema Diagram
  5. What are the Types of Slowly Changing Dimensions

An SCD is marked by the fact that, unlike sales-related events that might accumulate new entries over time, there can theoretically only be a single value for this dimension.

For example, there can only be one customer address, right? But what happens when he/she moves? How do you track it, now that the value in the ‘Address’ dimension has changed?

How do you keep the new address linked to all the activities linked to the previous address? The answer is to use one of the three types of SCD update actions.

Capture the changing data within the dimension over time with the help of dimensional model. Speak to Our Experts to get detailed insights.

OLAP vs OLTP: the Difference

In a data warehouse environment, users perform either Online Transaction Processing (OLTP: insertion, update, deletion) or Online Analytical Processing (OLAP: data retrieval for decision support).

Update performance, query performance, and schema simplicity are essential for the efficient use and maintenance of a data warehouse.

To achieve this, data is organized in a dimensional model called a star schema. In the star schema, the fact table is at the center surrounded by dimension tables.

What is OLAP (Online Analytical Processing)?

OLAP (On-line Analytical Processing) is categorized by comparatively small volume of transactions. Queries are repeatedly very complex and contain aggregations.

For OLAP systems an answering time is a success measure. On-line Analytical Processing (OLAP) applications are extensively utilized by Data Mining methods. OLAP database contains collected, historical data set, warehoused in multi-dimensional schemas generally a star schema.

What is OLTP (Online Transaction Processing)?

OLTP (Online Transaction Processing) is categorized by a huge number of small on-line transactions (INSERT, UPDATE, and DELETE).

The key importance of OLTP systems is placing on very fast query processing and retaining data integrity in multi-access backgrounds and measuring efficiency by total number of transactions per second.

In OLTP databases there is comprehensive and presenting data, and the schema used to accumulate transactional databases and catalogues is the entity model generally 3NF.

Typical Star Schema Diagram is as Shown Below:

slowly changing dimensions

An Engine That Drives Customer Intelligence

Oyster is not just a customer data platform (CDP). It is the world’s first customer insights platform (CIP). Why? At its core is your customer. Oyster is a “data unifying software.”

Explore More

Liked This Article?

Gain more insights, case studies, information on our product, customer data platform

Leave a comment