This post is aimed at a detailed comparison between Data Vault 2.0 vs Classical DWH, Building a Data Warehouse and also sees the pros and cons of each approach a well as it’s practical implications. Whether you are a business manager, an IT executive, or a student who needs to learn about the topic, you will learn new approaches throughout this guide and receive plain-English explanations of all those controversial terms without the hype.
The discussion between Data Vault 2.0 and traditional data warehouse modelling is centred around speed, scalability, flexibility, fleetness of foot and governance.
The classical model for data warehousing uses star and snowflake schemas. These approaches model into two kinds of tables: fact tables (measurable business elements) and dimension tables (non-measure, context like customer, time, product).
The traditional modeling aims to:
This is suitable for structured domains in which the system is well-understood.
Data Vault 2.0 has evolved from the original Data Vault brought to life by Dan Linstedt. Contrary to traditional modeling, Data Vault 2.0 divides data into three fundamental building blocks:
This decoupling enables organizations to ingest, integrate, and evolve data in a far more nimble way - even amidst shape-shifting organizational demands.
| Aspect | Classical DWH Modeling | Data Vault 2.0 |
|---|---|---|
| Goal | Business friendly analytics | Agile, scalable integration |
| Structure | Star/snowflake schema | Hub-Link-Satellite |
| Flexibility | Limited in adapting models | Highly adaptable |
| Focus | Query tuning | Data integration and agility |
| Historical Tracking | Usually restricted | Built-in through satellites |
Classical modeling has been utilized for many years thanks to its simplicity and its match with business reporting requirements.
Despite its implication, the classic model has weaknesses in current settings:
Data Vault 2.0 resolves more than a few problems with legacy approaches.
But there are also challenges to Data Vault 2.0:
| Characteristics | Classical DWH Modeling | Data Vault 2.0 |
|---|---|---|
| Ease of Use / Understood | My dad understands it for business users | Holy Grail, Complex, technical Cathedral |
| Flexibility | Clamp down like your conservative dad | Open-minded (but not too much) |
| Performance | Slow and steady | Let me put that summary on a left join |
| Agility | Yearly release | Cycle sprints daily |
| Historical | Part/half | Saturated |
| Solution Speed | Boiling Watch pot | Race car, but can attach carriage! |
| Best Fit | Horse with Carriage | Race car, but can attach carriage! |
Classical models work best when:
When to go for Data Vault 2.0:
An example of data integration is a retail company pulling together sales, customer and product information.
With classical modeling they establish a star schema with a sales fact table and dimensions for customers and products. Reports travel quickly, but adding new data sources takes months.
With Data Vault 2.0, the company constructs hubs for customers and products, links for sales transactions and satellites for things like addresses or pricing. New sources of data (e.g., online reviews) can be incorporated within short timeframes.
Some groups use a hybrid approach:
You can have the best of both worlds (Data Vault flexibility with classical schemas usability) by using this hybrid model.
The purpose is different: classical modelling where it was optimised for reporting and ease of report reading, Data Vault 2.0 where it’s been came up with to store the data so that can be blended or integrated as fast as possible yet still maintain a versioning history.
Not entirely. Several companies are applying Data Vault 2.0 for integration and classical models for reporting – a multi-layer or hybrid approach.
Data Vault 2.0 appeals to large enterprises that have to manage a lot of disparate systems that are in constant flux. Nevertheless, classical approaches are relevant in the case of sharply defined and constant surroundings.
And yes, DV 2.0 is NOT aimed at direct-reporting, it never was. (A reporting layer, such as star schemas are in most cases build on top for efficient analytics)
Not easily. Classical is nice for “set in stone” data, where Data Vault 2.0 can better handle semi-structured or changing sources.
Yes, but it takes a lot of strategy. For many organizations, Institutes a phased approach starting with DV 2.0 as foundation and leaving existing star schemas in place for reporting.
Data Vault 2.0 vs Classical Data Warehouse Modeling: Which One to Choose
Organization look out for their goals, resources, and data landscape. Classical models are good for performance and business question ease, but nothing can beat the flexibility of Data Vault 2.0. In fact, quite a few companies adopt the hybrid model and enjoy plenty of the best of both worlds.