Building Better Data Warehouses with Dimensional Modeling: A Guide for Data Engineers

Photo by Erin Doering on Unsplash
Photo by Erin Doering on Unsplash

Regarding system design for a data-intensive application, it usually comes with two options: write or read optimized. 

There isn’t a database design that fits and optimizes both writing and reading. Like all system design perspectives, no solution is right or wrong, while only pros and cons. As data professionals who work on data model design, a critical part of the role is to identify the use case and further identify which design principle should be applied. 

The data warehouse has historically acted as the layer serving data to the end users, and it’s the last mile to convert data to insights. Ralph Kimball developed one of the famous modeling design techniques called dimensional modeling. His The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition, is the most critical book for dimensional modeling.

Although big data and cloud computing technologies unblock us from using more computing power and cheaper storage, new or even experienced data engineers have overseen the data warehouse modeling design. Fewer people pay attention to types of slow change dimension (SCD), surrogated key, table granularity, etc. concepts anymore. 

Data model design is crucial for setting the foundation for any data warehouse system. I want to bring the community’s attention to the essential- Building Better Data Warehouses with Dimensional Modeling: A Guide for Data Engineers.

Why Should You Care About OLTP vs. OLAP

If you are familiar with the databases, two acronyms should not be strange: OLTP and OLAP.

OLTP stands for Online Transaction Processing, known for its design philosophy of normalization. In the early days, storage is expensive. The core idea of normalization is to reduce duplication and improve write efficiency.

For example, we could have a two-dimensional spreadsheet table. We have the product and the sub-type of its product. Both Alice and John have purchased the Honeycrisp apple at $1.99. The same attribute for the exact apple repeats twice for different customers — we have duplication. 

It becomes even worse when we’d need to perform an update/insert/delete operation. For example, we’d need to add which farm the apples come from, so we have to return to this table and repeat the entries thousands of times. 

OLTP Normalization to Remove Duplicates | Image By Author
OLTP Normalization to Remove Duplicates | Image By Author

To resolve such duplication and improve efficiency, we can accomplish this by normalization — making a separate product table and moving all its related fields to that table. In the original table, only keep an identifier to reference the row in the newly created table. 

This saves the storage spaces and reduces the number of rows to touch if an update/insert/delete operation needs to be performed.

So what’s the catch on the OLTP side? It’s not for analytics pattern queries. 

The analytics pattern queries focus on query efficiency in reading data using Select/Filter/Aggregation. The lack of such support for such query patterns leads to OLAP. 

OLAP stands for Online Analytical Processing, known for its design philosophy of denormalization. The core idea of denormalization is to reduce complex business logic and big table joins (later, we call it fact to fact table) at run time. 

Preparing and writing data often happens beforehand within an ETL process, which usually takes longer to execute and precompute the business logic into the final table.

Returning to our data example of Apples, the spreadsheet we shared before normalization is an excellent example of analytics design. We have avoided joins between spreadsheets and can perform select/filter directly. 

Most of the time, data engineering aims to serve an OLAP data warehouse for end users. Unlike OLTP, which avoids duplication as much as poss, The design principle of OLAP is — redundant (denormalization) isn’t bad. We sacrifice the storage cost but get faster query time at run time. 

4 Steps For Successful Dimensional Model Design

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition, is the most critical book for dimensional modeling. The book covers four essential steps for a successful data warehouse: 

1. Choose the Business Process

Through my data engineering career path, I learned to find and stick to the business use case is the most important thing. You can have all the fancy tools, milliseconds latency real-time processing, and a powerful distributed compute query engine. No use case is a castle in the air. 

Regardless of how thoughtful your design is, consulting with the end users is always a wise first move if you are also the end user (usually unlikely for data engineering). We can interview end users on how they parse and comprehend the data with their domain knowledge, how their current reports are generated, and understand the pain point in their process. 

Kimball’s Enterprise Data Warehouse Bus Architecture is also an excellent technique to bridge the business use processes and the standard dimensions. The goal is to evaluate what sizes are shared across the fact table. Leveraging the bus matrix technique streamlines the design decision on designing dimensions at a later stage and helps you visualize the overall structure. 

Full Kimball Data Warehouse Bus Architecture coverage is available in The Data Warehouse Toolkit, Third Edition, including sample bus matrices for 12 industry case studies.
Full Kimball Data Warehouse Bus Architecture coverage is available in The Data Warehouse Toolkit, Third Edition, including sample bus matrices for 12 industry case studies.

2. Declare the grain

What is the gain? It refers to the lowest level of information kept in a table. 

How do we know which grain is needed? We’d need to consult with our users to make such a decision. Usually, the more detailed information is provided, the more flexible it is within the table. Only having aggregated data is impossible to get the detail line level. For example, we can get the weekly sales data from a daily aggregation, but we can’t break down the weekly sales into daily easily. 

The aggregate view is faster for queries but needs more detail. On the other hand, the lower grain can be aggregated later to provide the same insights, but it can take additional time at run time. It’s critical to confer with end users and help them understand the pros and cons and potential future needs. 

I recommend starting the grain as low as possible because more granular data can be aggregated later, but not the other way around. You can make a higher-grain fact table using the existing one if a higher grain is needed.

3. Identify the Dimensions

Once the grain is identified, dimensions should be the next to locate. Dimensions are the cornerstone of the fact table.

Dimensions are to keep the fact table with more sense for the business. Typically dimensions are nouns like date, category, products, etc. They serve the purpose of the slice & dice. End users can use dimensions to focus on a specific data area or aggregate multiple fields to observe potential patterns. For example, what are the top 3 types of apples in our previous example?

Dimensions often contain much less than the fact tables. Suppose you are familiar with distributed computing or have worked on Spark physical plan. In that case, dimension tables are usually suitable for broadcast join, which means this dataset is small enough to send to every node for joining efficiency. 

The slow change dimension (SCD) is a core dimension designing concept. Although there are over six types of SCD, the most critical ones are SCD Type 2, given its wide adoption in the industry. 

If a row in the dimension value has been modified, how to track such change in your dimension table design? 

That’s the beauty of SCD Type 2. We’d add another row with the same business key. The newly added row includes the unchanged fields and the modified fields. To know which row is present and which is the past, we’d add the start and end date time for the valid period of a specific row. For example, if the supplier moved from CA to IL, we can still track it in the slow-changing dimension. 


Slowly Changing Dimension | Image from

The Suppier_Code — ABC is usually the business key supplied from an OLTP system. To keep a primary to join with the fact table later, we’d need to create a surrogate key — Suppkier_Key- unique from this dimension. 

4. Identify the Fact

The fact table is the “soul” of dimensional modeling. The fact table keeps the core business in the fact table. Most fact table rows are numeric only, such as total sales quantity, transaction fee, and profit.

There are multiple types of fact tables. You can build a transactional-based fact table or a snapshot fact table like monthly sales. 

The core idea of the fact table is to track the data change for a given period. For example, assume Alice has bought the Honeycrisp apple. Then the next day, she returns it because she found a worm inside the apple. 

  • If we take the transaction base view, we will log two rows. The first row is the profit for the $2.99, and the second is -$2.99 (assuming no extra operation cost) since she returns the apple. 
  • If we take a snapshot of the monthly base view, Alice’s purchase won’t impact the business since the net profit from Alice is 0. 

The fact table is the tip of the pyramid. It requires all the foundation work to be ready. So having a solid foundation is critical before jumping into the fact table design. 

Should I Choose Star Schema and SnowFlake Schema?

The choice of Star schema and Snowflake schema for the data warehouse could be more straightforward for people familiar with the OLTP design and trying to put some level of normalization. 

Start schema should be preferable in the majority of cases. There are a few reasons:

  • We’d want to avoid extra join, which slows down your query performance
  • The more relationship among dimensions, the more complicated to make manage and maintain data integrity
  • End users usually don’t have the same familiarity with data schema, and designing the data warehouse should be simple and user-friendly.
  • The extra cost to store additional data is minimal. 

An exception we shall consider Snowflake schema is to save some cost or if the dimensional itself updates frequently, and you need to add normalization to reduce the number of fields to be touched. However, the first goal is to reduce the join to reduce the query time for the end users.

Why Not Keep Everything on a Single Table?

It is also an option that stores all the fields in a single table instead of separating fact and dimension tables. 

We could go with a spreadsheet-like single table, and the query could become even faster with columnar storage like Parquet

The critical criteria to consider is — a dimension table is also shared across multiple fact tables. It would make things easier to manage as the dimension has been referenced in various places.

How about joining? Is dimension to fact table join slow? We have mentioned earlier that “Dimensions often contain much less than the fact tables.”. For distributed computing, the most efficient way is to send the smaller dataset to every single node to avoid a large volume of data getting shuffled. In this case, the join of the fact table is usually minimal, and it’s the engineer to evaluate the trade-off between query performance or data management effort. 

Final Thought

Dimensional modeling and its principle have been in the industry for over 40 years. It has been proven to be a typical design pattern for OLAP use cases. Although the big data era has brought us much more powerful distributed computing engines than ever, the fundamental design principle for OLAP shouldn’t fade away. Getting the design of a data project right is always crucial rather than throwing extravagant resources.  

I hope this article can bring awareness to more data engineers who overlook the data warehouse design to rethink design first than chasing new tools. 

About Me

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