However, normalized and dimensional data modeling techniques are not designed to handle rapid changes such as these. Data vault modeling helps to solve this problem – providing organizations with more speed and flexibility for their analytics requirements.
Basic Data Vault Modeling
Data Vault is a detailed-oriented data modeling approach that provides flexibility and agility as data volumes increase and/or become more complex and distributed. These challenges can be addressed by businesses to help them make better business decisions.
These benefits were made accessible by the Data Vault approach, which Dan Linstedt developed in the 1990s. Data Vault 2.0 followed in 2013 and introduced a range of enhancements centered around NoSQL, Big Data, and integrations for semi-structured and unstructured data.
Linstedt’s goal was to make it easier for data engineers and data architects to create a Data Warehouse quicker. With a shorter implementation timeline and a more effective way to address the business’s needs, Linstedt was able to do so.
What are the benefits of the Data Vault approach to business?
This is the main benefit: The shorter the implementation cycle, the better. Business requirements for Data Warehouse and ongoing enhancements (through the introduction of new sources) are met with shorter cycles. This helps to avoid shifting goals that could impact budgets.
Because of its flexibility and scalability, many organizations will choose to use the Data Vault approach. This agile approach to project management is extremely popular and closely aligned with the Data Vault concepts. Combining the two can provide real agility to any business’s data strategy, eliminating the need to scale up or down data storage or processing capacity.
Parallelization is another point to be aware of. Data Warehouse data must be synchronized at fewer points when it is loaded. This allows for faster data loading, which is a big help when dealing with large data volumes and real-time data inserts.
Data Vault’s historical data tracking makes it possible to audit data models without any additional complications. A sophisticated Data Warehouse structure allows for easy auditing and provides security features that allow compliance with data security requirements.
What are the responsibilities?
These strengths are great, but Data Vault, just like other data modeling approaches, has limitations that organizations need to be aware of.
Most obvious is a large number of data objects, compared to other methods, such as tables and columns. The Data Vault method separates information types.
This can lead to a greater upfront modeling effort and more manual or mechanical tasks required to create a flexible, detailed data model that includes all its components.
These are the challenges that organizations must address if they want to avoid manual labor during the modeling process. Automation is the key to this.
How does automation help them?
There are many layers within the Data Vault:
- Source systems are where the data will originate or be created;
- A staging area that receives data from the source system and models it according to its original structure.
- The core data warehouse contains the raw vault. This layer allows data to be traced back from the original source system data.
- A business vault is essentially a semantic layer that implements business rules.
- Data marts are structured according to the needs of an organization. For example, a marketing or finance data mart would contain relevant data for analysis purposes.
Automation is most effective at the staging area and raw vault layers. Here automation can help data architects to save time and increase the efficiency of their data vault approach.
How do businesses build on the Data Vault approach to data marketing?
Organizations shouldn’t be held back by data inefficiency. It is now possible to create a data ecosystem that integrates technology and software. This will support the overall data strategy over many years. The use of tools that complement one particular data modeling technique can help to improve the performance of individual analysts and experts who rely on a stable data environment for their daily work.
Data Vault modeling can be an integral part of this environment. A robust approach to maximize the benefits of a Data Vault approach will be a benefit to those working at the coalface. This will allow them to run analytical models and workflows with much greater performance, allowing them to optimize the value of their data quickly.
Data specialists can rest assured that their data is auditable at all times, that large amounts of data can be loaded without problems and that historical queries can be reproduced as required. This will allow organizations to make better business decisions and deliver better results for their customers and employees.