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How are Type 1, Type 2, and Type 3 slowly changing dimensions different?

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Data warehousing is the process of collecting, organizing, and analyzing data from various sources to support business intelligence and decision making. One of the key aspects of data warehousing is how to handle changes in the data over time, especially in the dimensions that describe the attributes of the data. These changes are known as slowly changing dimensions (SCDs), and they can affect the accuracy, consistency, and performance of the data warehouse. In this article, you will learn about the three main types of SCDs and how they differ in terms of implementation and impact.

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Chidvilas Tammireddy

1 Type 1 SCD

A type 1 SCD is the simplest and fastest way to deal with changes in the dimension data. It simply overwrites the old value with the new value in the dimension table, without keeping any history of the change. For example, if a customer changes their name or address, the dimension table will only store the latest information. This method is suitable for minor or correctional changes that do not affect the historical analysis of the data. However, it also means that you lose the ability to track the changes over time and to link the dimension data with the corresponding facts.

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Chidvilas Tammireddy
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I used this approach when the changes in the data did not require historical tracking. This was particularly useful for correcting data errors or updating non-critical attributes where retaining the previous data was unnecessary. For instance, if there was a correction needed in the customer name, we simply updated the record directly.

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Surya Teja Anukula

Software Test Engineer | ISTQB Certified | SQL | Power BI | POSTMAN-API | ETL, Data warehouse, Data Migration
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#Type 1: Overwrite >Change Method: Updates old data with new data. >History: No history is kept; only the latest data is shown. #Type 2: Add New Row >Change Method: Adds a new row with the updated data and a timestamp. >History: Keeps full history of changes, showing when each version was valid. #Type 3: Add New Column >Change Method: Adds new columns to store historical data. >History: Keeps limited history, typically the current and one previous value. These types handle changes in data differently based on whether you need to keep history or just update the current data.

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2 Type 2 SCD

A type 2 SCD is the most common and complex way to handle changes in the dimension data. It preserves the history of the changes by creating a new record in the dimension table for each change, with a unique identifier and a start and end date. For example, if a product changes its price, the dimension table will store both the old and the new price, with different identifiers and date ranges. This method allows you to perform historical analysis of the data and to see how the changes affect the facts. However, it also increases the size and complexity of the dimension table and requires more maintenance and processing.

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Chidvilas Tammireddy
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This method was employed when it was crucial to maintain a history of data changes. For example, when customer address information changed, I created new records with different versioning to keep a history of all previous addresses. This approach helped in analyzing customer movement trends and maintaining accurate records for compliance purposes.

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3 Type 3 SCD

A type 3 SCD is a hybrid way to manage changes in the dimension data. It keeps some history of the changes by adding a new column in the dimension table for each change, with the previous value. For example, if a salesperson changes their region, the dimension table will store both the current and the previous region in two columns. This method enables you to compare the data before and after the change and to analyze the impact of the change. However, it also limits the number of changes that can be tracked and does not capture the time of the change.

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Chidvilas Tammireddy
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I utilized SCD Type 3 for situations where only the most recent change and one previous version were required. This was useful for tracking certain attributes like a change in account status (e.g., from 'Active' to 'Inactive') where we needed to see both the current and prior status without requiring a full historical record.

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4 Choosing the right type

There is no definitive rule for choosing the right type of SCD for your data warehouse. It depends on several factors, such as the nature and frequency of the changes, the business requirements and objectives, the data quality and consistency, and the available resources and tools. You may also use different types of SCDs for different dimensions, depending on their relevance and importance. The main goal is to balance the trade-offs between simplicity and complexity, accuracy and performance, and current and historical data.

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Chidvilas Tammireddy
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I found it useful to start with SCD Type 3 before moving to SCD Type 2 when handling frequently changing data. This approach allowed us to test how keeping some history would affect performance and accuracy before fully implementing a complete historical tracking system. It helped us find a good balance between performance and the need for accurate historical data early in the project.

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5 Implementing SCDs

Implementing SCDs in your data warehouse requires careful planning and design, as well as appropriate tools and techniques. You need to identify which dimensions are subject to changes, what kind of changes they are, and how they affect the facts. You also need to define the logic and rules for detecting and handling the changes, as well as the keys and attributes for the dimension tables. Moreover, you need to use a data integration tool or a programming language to automate the process of loading and updating the dimension data, as well as to ensure data quality and integrity.

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6 Benefits of SCDs

SCDs are an important part of creating a robust and reliable data warehouse that can support different types of analysis and reporting. By using SCDs, you can capture changes in the dimension data over time, maintain the accuracy of the data warehouse, improve the granularity and richness of the data warehouse, and meet business needs. These benefits help avoid data anomalies and errors, enabling more insights and discoveries as well as providing relevant and timely information.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How are Type 1, Type 2, and Type 3 slowly changing dimensions different?

1

2

3

4

5

6

7

1 Type 1 SCD

2 Type 2 SCD

3 Type 3 SCD

4 Choosing the right type

5 Implementing SCDs

6 Benefits of SCDs

7 Here’s what else to consider

Data Warehousing

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How are Type 1, Type 2, and Type 3 slowly changing dimensions different?

1

2

3

4

5

6

7

1 Type 1 SCD

2 Type 2 SCD

3 Type 3 SCD

4 Choosing the right type

5 Implementing SCDs

6 Benefits of SCDs

7 Here’s what else to consider

Data Warehousing

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