Types Of Slowly Changing Dimensions in Data Warehouses

Data warehouses are crucial for managing large volumes of data in a singular location. Most data warehouses offer you comprehensive features for storing and processing data. However, with time, data can change drastically. Updating your data warehouse becomes essential, as most of your operations and strategies depend on it.

To capture and retain changes made to your data, you can implement different SCD Types in your data warehouse. This article provides you with a thorough understanding of SCDs, their benefits, and why you should consider using them.

What are Slowly Changing Dimensions (SCDs)?

Slowly Changing Dimensions are a key concept in a data warehouse. They are designed to track and manage historical changes in dimension data tables over a period of time. Dimension tables allow you to categorize your data into attributes that support the fact tables in the data warehouse.

Since dimensions provide you with context for analyzing datasets, they are usually considered static. However, as you update source data and import these changes, the dimensions must keep track of modified data. Hence, Slowly Changing Dimensions are used in data warehouses to ensure historical changes are retained without overwriting existing data.

Different types of SCD in a data warehouse help you capture and store different states of the same dimension data. Read on to understand which SCD Type is most suitable for various columns and tables of your dataset.

Need For Slowly Changing Dimensions in Data Warehouses

SCD types in a data warehouse allow you to retain previous versions of your dataset while updating the latest modifications. This creates a balance between maintaining detailed historical records and efficiently storing vast volumes of data.

Employing different SCD types in your data warehouse with advanced machine learning algorithms enables you to study past patterns and trends. This study can uncover insights into pricing patterns, seasonal demand, and customer preferences, as well as forecast future demand.

Different types of SCD in data warehouses can give you an accurate timeline for various changes made to your dataset. This way, you can authenticate the validity and integrity of the data that you will be using for further analysis. You can also uncover errors promptly through a point-in-time analysis and take necessary actions to prevent poor-quality data from seeping into strategies.

Types Of SCDs in Data Warehouses

Understand the types of SCDs in a data warehouse with examples:

Type 0

This SCD Type should be used for attributes that do not change and must remain constant always. For instance, you have a dataset titled “Employees”. Here, SCD Type 0 can be applied to columns such as Employee ID, Employee Joining Date, or Employee Resignation Date.

Type 1

For Type 1 SCD, whenever there are changes in dimensions, it is overwritten over existing data. This SCD does not maintain historical records, making it ideal for situations where you need to correct or update static information. For example, columns like Employee Department or Employee Address in the Employees dataset can be configured to SCD Type 1. These attributes do not change quite often, and you do not need to keep track of previous data.

Type 2

This is one of the most widely used SCD Types in a data warehouse. Type 2 SCD retains historical records of every change made to the dataset. For every dimension that is altered, this SCD will create a new row to the table along with a surrogate key. This key is independent of the table’s original primary key, helping you differentiate between current and historical records.

Drawing from the previous example, you can apply Type 2 SCD to columns such as Employee Salary and Employee Designation. Keeping historical records for such columns comes in handy when you are tracking an employee’s growth in the organization.

Type 3

SCD Type 3 is well-suited for dimensions where you want to keep limited history for certain columns in your dataset. This SCD adds a new column to the table when changes are made. However, this column does not have a separate key, unlike Type 2 SCD. The new column simply gets appended and has the same primary key as the rest of the table.

Type 3 SCD can be applied to columns such as Employee Last Name or Employee Location in the Employees dataset. Female employees of the organization may change their surnames after marriage, or employees may opt for transfers to another location of the organization. Thus, for these one-time changes, it is best to opt for Type 3 SCD.

Type 4

SCD Type 4 is another often used dimension in a data warehouse. You can apply this to attributes that change rapidly. Type 4 SCD creates a separate historical table, where all the previous records are stored away from the currently active record.

For instance, if you have a Customer database, you can configure SCD Type 4 for columns, such as Customer Order Items or Product Prices. Having a different table for past purchases and product prices allows you to analyze customer preferences and demand patterns in greater detail.

Type 6

This SCD Type is a combination of Type 1,2 and 3. It combines the row-based history of Type 2 with the column-based history of Type 3. This is particularly useful in situations where you need to track current, historical, and static data at the same time. For example, you can use Type 6 to track Employee’s Highest Education, Employee Location, and Employee Designation to determine colleges for recruiting freshers.

Limitations Of Slowly Changing Dimensions (SCDs)

• Maintenance Efforts: Managing different types of SCDs in a data warehouse requires significant manual effort. You also need to conduct a comprehensive data audit to ensure the data is of high quality and consistent format.‍
• Increased Storage: Each change captured by various SCD types takes up storage space in the data warehouse. This may not only hinder the data warehouse's performance over time but also add to storage costs.‍
• Complex Queries: As the number of dimensions grows, data retrieval becomes difficult. You may need to write complex queries, which may be prone to human errors and produce inaccurate results.

How to Choose The Right SCD For Your Data Warehouse?

Selecting the appropriate SCD types for your data warehouse involves carefully assessing your business requirements. You must consider how frequently you update your datasets and how this subsequently affects storage space and the performance of your ETL pipelines and data warehouse.

If your attributes do not change significantly over time, it is best to select SCD Type 0. However, if the dimension table requires minor updation occasionally, you can go with either SCD Type 1 or 3. Choose between SCD Types 2, 4, and 6, depending on how often you modify your source data. You must also gauge how extensive you want the historical data to be.

How to Implement Slowly Changing Dimensions in Data Warehouses?

Before implementing multiple SCD types in your data warehouse, you must have a strategic plan in place. This plan should involve all relevant stakeholders, like your data engineers, analysts, and team members who require access to the data warehouse.

You must first start by assessing the current database dimensions and attributes in your dataset and map out all the relationships between them. This way, you can discover if a Type 1 SCD has accidentally been marked as a Type 2 or 4 SCD.

Some data warehouses like Snowflake allow you to use SQL commands like MERGE and MERGE INTO. These enable you to implement SCD Type 2 or 3 in particular columns of your dataset. A few data warehouses also provide the option to annotate SCD attributes with flags or timestamps, giving you an accurate estimate of modifications made. For such columns, it is best to use an SCD Type 4 or 6.

Your SCD implementation plan must also outline how to handle existing columns that do not have historical tracking enabled. If any new columns are added in the future, your team should know which SCD Type is most suitable in that case.

Periodically assess all types of SCDs in your data warehouse and ensure you are following the best practices for data extraction and ingestion. One of the best ways to conduct data extraction is by using a robust data movement platform like Airbyte.

Airbyte is a data integration and replication platform that provides over 400+ connectors. It helps you to easily build a data pipeline from your preferred source to a desired destination in a few minutes. If you do not find a connector from its extensive library, you can utilize its no-code Connector Builder feature. This custom connector builder comes with an AI-assistant that pre-fills some of the mandatory configurations, ensuring your pipeline is built without writing code.

Airbyte also provides multiple features to help you manage your SCDs. You can configure Change Data Capture for your pipelines, capturing incremental changes made at the source and replicating it to your destination.

While creating your pipeline, you can choose from various sync modes that Airbyte offers. The platform also presents detailed logs after every sync, allowing you to gauge which type of SCD you can implement for particular tables and columns. This way, you can keep track of historical records and modifications made to your datasets through your data pipeline platform.

Can We Combine SCD Types?

It is possible to combine different SCD types in a data warehouse. For instance, you have a large customer dataset that you must update periodically. However, some columns, such as customer mobile number or email address, do not require historical data tracking. For these columns, you can apply SCD Type 1.

You can implement SCD Type 2 for columns in the customer dataset that require frequent updation. These can include attributes like the latest order date, the purchase amount, or the quantity of items purchased. Keeping historical data is essential here because you can conduct predictive analytics and infer purchase patterns.

You can use different techniques to combine various types of SCD in the data warehouse. Apply SQL queries, such as MERGE, to bring together various SCD Type changes in one operation. Your organization can also use tools like dbt to create a dbt model that can handle complex SQL queries and transformation while providing an easy interface.

Conclusion

Slowly Changing Dimensions track changes in dimensional data over time, making them essential for supporting various types of analysis. The article illustrates the various SCD types in a data warehouse with examples, helping you implement the right one for your organization’s datasets.