What is a Data Vault?
In contrast to traditional data warehouse models, the innovative data vault methodology is designed for storing raw, unprocessed data without applying business rules. The model relies on a more agile approach of transforming and viewing results only when needed in department-specific data marts. This can help tackle difficult use cases while still maintaining efficiency from a management level. However, this can be difficult to implement at times due to its unique nature.
Data Warehouse vs Data Vault
A traditional data warehouse requires raw data to be ready before it is loaded. The ETL process pulls raw data from a source, transforms it to fit warehouse schema standards for storage, and loads the standardized format into the database so users can access their desired information whenever they need it.
What would you expect to find in a data warehouse?
A place where everything is clean, valid, and predictable. But a data warehouse comes with a few limitations. For example – it is not possible to have different data in the warehouse than what is stored on an operational database which can lead to inconsistencies between systems.
Now let’s have a look at the data vault process!
The ETL process pulls raw data from a source as normal. It performs some basic transformations such as eliminating corrupt values before loading that information into its own specific area of the warehouse known as the “raw data vault.“
A data vault tackles the problem of transformation by moving the transformation to a later stage in the process.
The warehouse stores business rules separately from the data, which is a good security measure. When required, these are applied to create a new version of the data – which is then stored in another part of this facility known as the “business vault“. You can access data through virtual marts created by using views from within the business vault.
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| Attribute | Data Warehouse | Data Vault |
|---|---|---|
|
Define in a line |
Stores the truth |
Stores the facts |
|
ETL Involved |
Yes |
No |
|
Security |
High |
Medium |
|
Data Quality Involved |
Yes |
No |
Reasons to choose a Data Vault
- A data vault offers the most advantages in terms of simplicity and conciseness when your data comes from many sources, or if the data changes constantly. You can easily add attributes and changes to one source system that won't affect the data that is stored in other sources – even if you're adding an attribute only in one place!
- It is a great way to keep track of every change that happened with your data. As the load time is required for each row, you can easily audit who changed what and when it was done. You also get an activity log that tracks all changes in chronological order - perfect if something goes wrong!
- Data vault not only allows quick data loading but also makes it possible to load many tables in parallel. This results in faster and more efficient queries that provide a better customer experience.
Why enterprises don't choose a data vault
- You have to load your data directly into the tool you're reporting with.
- You only have one source system and relatively static data.
Data Vault - The powerful combination of Lyftrondata
Lyftrondata handles today’s data challenges and enables enterprises to stay on top of an avalanche of information. The modern architecture, enterprise-class integration, and AI-driven automation enable users to bring together all their data, at any scale, from different sources in a data vault in a matter of minutes for advanced analytics.
The new Lyftrondata architecture brings the full power and confidence of the next-gen data vault to fast-growing enterprises and enables them to perform with no limitations. With the agile-based infrastructure, you could build huge applications easily and go from raw data to powerful insights in a matter of minutes.
