Dimensional modeling and data vault modeling are two common approaches for designing data warehouses, but they have different goals, principles, and benefits. In this article, you will learn what are the main differences and similarities between these two modeling techniques, and how to choose the best one for your data warehouse project.
Dimensional modeling is a data warehouse design method that focuses on creating a simple and intuitive structure for business users and analysts. It organizes data into facts and dimensions, where facts are numerical measures of business events, and dimensions are descriptive attributes that provide context and detail for the facts. For example, a sales fact table may contain measures such as quantity, revenue, and profit, and dimensions such as product, customer, date, and location. Dimensional modeling uses a star or snowflake schema, where a fact table is surrounded by dimension tables, forming a star-like or snowflake-like shape.
Every organization is unique and yet if you look at the general questions around business asked, those are amazingly standard. Usually it is looking at some metric - say Revenue, by different segments (say by Time, by Product, by Country etc.) The segments are also known as dimensions. In a SQL world you can imagine it as your GROUP BY clause. The metrics form the measurement. In a data model world measurments are housed in a central FACT table, segments are housed in lookup/reference table. These come together in a STAR or SNOWFLAKE schema, STAR being the simpler of the two.
Dimensional model could be Star schema or snowflake schema. In Star schema, one fact table in the middle is connected to one or many dimension tables. In this model, one dimension can not be connected to other dimension. In Snowflake schema, as name suggests, it has a design of snowflake. One fact is connected to one or many dimensions. Each of these dimensions can be further normalized (broken down) in one or many dimension. Out of these 2, which approach to be taken is very business problem specific. Star schema provides simpler join and faster analysis whereas Snowflake schema provides better maintenance aspect since the tables are normalized more than Star schema
Choosing the right DW approach actually depends on what your existing data-set up and customer needs. If clients are looking for analytical insights and you know your data is consistent then Dimensional modelling(think of it has ETL) is the way to go. You build them semantic models and have them do self-service ( In the azure world , synapse provides the best tools ) however if you data set-up has constantly evolving, in-consistent data where the focus is on data auditing & adherence to compliance , data engineering and not on insights then data vault is the way to go ( think of a medallion architecture where you have bronze layer aka raw data vault layer where ELT happens, Databricks Lakehouse Platform does the job here).
Data vault modeling is a data warehouse design method that focuses on creating a flexible and scalable structure for data integration and historical preservation. It organizes data into hubs, links, and satellites, where hubs are tables that store the unique keys of business entities, links are tables that store the relationships between entities, and satellites are tables that store the attributes and history of entities or relationships. For example, a customer hub may contain the customer ID and a load date, a customer-order link may contain the customer ID and the order ID, and a customer satellite may contain the customer name, address, phone, and email. Data vault modeling uses a normalized schema, where tables are connected by foreign keys, forming a network-like shape.
Hub are always parent table, and represent a domain, having surrogate key as PK along with business key,load date and data source, no additional attributes. A satellite may have all other attributes for that hub or a satellite can be divided into two or more table considering mandatory/less frequent change prone fields and More frequent change prone or non mandatory fields or considering the cardinality of data changes for group of attributes. However, PK will be a composite key Hub’PK +load date A satellite table can be attached to hub or link (if required). Link can have their on surrogate key as primary, PK from hubs as FK, load date and data source.
Dimensional and data vault modeling differ in their purpose, structure, performance, and flexibility. Dimensional modeling aims to provide a user-friendly and query-optimized data warehouse that supports business intelligence and analytics. It uses a denormalized schema to reduce the number of tables and joins, but may introduce data redundancy and inconsistency. This model typically offers faster query performance and simpler ETL processes, but may require more storage space and more frequent updates. It is also less flexible and more dependent on the business requirements and the data sources, meaning it may need to be redesigned or rebuilt when the requirements or the sources change. Conversely, data vault modeling seeks to provide a data-centric and change-resistant data warehouse that supports data integration and governance. It uses a normalized schema to increase the number of tables and joins, but avoids data redundancy and inconsistency. This model offers slower query performance and more complex ETL processes, but may require less storage space and less frequent updates. It is also more flexible and more independent of the business requirements and the data sources, allowing it to accommodate new or changing requirements or sources without affecting the existing data warehouse.
Dimensional modeling and data vault modeling have some similarities, such as their modular components which can be reused and extended. Dimensional modeling uses facts and dimensions, while data vault modeling uses hubs, links, and satellites. Both methods are able to preserve historical data and track changes over time; dimensional modeling does this through slowly changing dimensions or type 2 dimensions, while data vault modeling does this through satellites and load dates. Additionally, these two methods can be combined with other methods to create a hybrid data warehouse architecture; dimensional modeling can be used as the presentation layer or the data mart layer, while data vault modeling can be used as the staging layer or the integration layer.
Good to add here is that the dimensions and facts for a dimensional model can be derived from the hubs, links, and satellites in a Data Vault model. Merge a hub and its satellites to get a dimension. Merge a link and its satellites to get a fact. The process of translating a Data Vault model to a dimensional model can be standardized and automated.
you can use both at the same part but for different layers data vault for building a scalable data warehouse star schema for the representation layer everyone has some advantages that you can use for specific goals . for example pit table are completely useful for dimension tables and non-historical links for fact tables
When it comes to determining which modeling technique is best, there is no single answer as it depends on the quality, volume, complexity, and usage of the data. For instance, if the data sources are reliable and consistent then dimensional modeling may be more suitable whereas data vault modeling may be more suitable if the data sources are unreliable and volatile. Similarly, if the data volume is small or moderate then dimensional modeling may be more efficient while data vault modeling may be more scalable when dealing with large or growing volumes of data. Additionally, dimensional modeling may be better for low to moderate complexity in data while data vault modeling may be more adaptable for high or increasing complexity. Lastly, dimensional modeling may be more convenient when the data is mainly used for reporting, analysis, or visualization while data vault modeling may be more robust when used for integration, governance, or auditing. The best approach is to assess your data warehouse goals and requirements and compare the advantages and disadvantages of each technique in your specific context.
I don't think the idea of choosing one or the other is really appropriate. I think at the end you will have layers in your DW architecture. Highly likely your top layer ( where users will query from using various BI tools) will be moee closer to dimensional modelling principles and the supporting (fully auditable) layer would be closer to a DV model. which layer you build first is down to what is required and how reliable/auditable your sources are
Data-vault resolves the problem of cardinality in data warehouse, if 1:1 relation got converted into 1:m in translational system, we do not need to change in data vault dwh. It also helps in time travel analysis. However, the architecture becomes more complex and data load pipeline will gets more complex and lengthy. and so whole ecosystem is complex and resource extensive.