Data Vault 2.0 vs Classical Data Warehouse Modeling: A Detailed Comparison - foulegold/media GitHub Wiki
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 itโs 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.
Data Vault 2.0 vs Traditional Data Warehouse Models
The discussion between Data Vault 2.0 and traditional data warehouse modelling is centred around speed, scalability, flexibility, fleetness of foot and governance.
- Data Vault nearest neighbour is Classical (craftsmanship and business-readability) but DV2.0 emphasis performance and quickly changing system.
What is CWM?
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:
- Ensure consistency and integrity
- Optimize for query performance
- Provide business-friendly structures
This is suitable for structured domains in which the system is well-understood.
What is Data Vault 2.0?
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:
- Hubs: represent unique business keys
- Links: representing the relations of business keys
- Satellites: keep the metadata and history
This decoupling enables organizations to ingest, integrate, and evolve data in a far more nimble way - even amidst shape-shifting organizational demands.
Core Philosophy of Each Approach
| 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 |
Benefits of Traditional Data Warehouse Modeling
Classical modeling has been utilized for many years thanks to its simplicity and its match with business reporting requirements.
- Business User Friendly: The star schema is analyst friendly.
- Performance optimized: Fast and clean queries.
- Mature Within: There is ample tooling, documentation and practitioners.
- Consistent: Measures provide consistency in definitions throughout the enterprise.
Limitations of Classical Modeling
Despite its implication, the classic model has weaknesses in current settings:
- Unwieldy when it comes to reconfiguring for evolving business rules
- Semi-Structured or Unstructured data was difficult to integrate
- ETLs that are heavy in time to update the model
- High upfront cost and the implementation takes forever
Advantages of Data Vault 2.0
Data Vault 2.0 resolves more than a few problems with legacy approaches.
- Highly Flexible: Can be tailored to changing business rules or systems faster
- History of events: All changes collected in the course of time by themselves
- Scalability: Good with big data implementations and distributed systems
- Agile Development: Iterative and Incremental Delivery from Agility
- Power of Integration: Efficiently manage conflicting sources of data
Limitations of Data Vault 2.0
But there are also challenges to Data Vault 2.0:
- More complex model structure than star schemas
- Needs lamination for business reporting
- Greater learning curve for teams that are new to the methodology
- Possible performance overhead before data is changed to be fit for reporting
Pros and Cons Side by Side
| 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! |
When Classical Data Warehouse Modeling Was Right For You
Classical models work best when:
- The Business Domain is Stable and Well-Defined
- Users prioritize fast reporting performance
- The company has established teams and tools for BI
- The simplicity of the presentation needs not to bow down to an historical tracking
When to Choose Data Vault 2.0
When to go for Data Vault 2.0:
- To integrate more than one system and to support multiple sources
- The environment changes frequently
- Historical traceability and auditability is a must have
- The business needs a solution for big data that can expand efficiently
- Agile or iterative approach to delivery is desired
Practical Example: Retail Company
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.
Hybrid approach in Building Data Warehouse
Some groups use a hybrid approach:
- Leverage Data Vault 2.0 to Integrate and track history
- Construct classic star schemes as reporting on top of those
You can have the best of both worlds (Data Vault flexibility with classical schemas usability) by using this hybrid model.
Key Takeaways
- Strong business readability and performance with classical modeling
- Data Vault 2.0 is strong on scalability, agility and tight integration
- Choice depends on company priorities โ stability vs adaptability
- Hybrid approaches can offer the best of both
FAQs
What distinguishes Data Vault 2.0 from the classical approach?
The purpose is different: classical modelling where it was optimised for reporting and ease of report reading, Data Vault 2.0 where itโs 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.
Will Data Vault 2.0 replace standard data warehousing models?
Not entirely. Several companies are applying Data Vault 2.0 for integration and classical models for reporting โ a multi-layer or hybrid approach.
Which is the best model for big business?
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.
Will Data Vault 2.0 impact reporting performance?
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)
How do old models fit on un-structured data?
Not easily. Classical is nice for "set in stone" data, where Data Vault 2.0 can better handle semi-structured or changing sources.
Can we move from the classical model to Data Vault 2.0?
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.
Conclusion
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.