10 high-level benefits of using Data Vault

What is Data Vault?

Data Vault is one of the best-known data warehouse implementation methodologies

Data Vault is used to solve many typical challenges in data warehousing. It standardizes development, enables workflow automation, and brings flexibility and scalability to the data warehouse over its lifecycle. It helps in making your data warehouse or data lakehouse more resilient. 

Read on to understand the high-level benefits of utilizing Data Vault methodology in your data warehouse or data lakehouse implementation:

1. Data Modeling Capabilities
2. Supports the core Data Vault object types
3. Automation of Data Warehouse engineering
4. Reusability and unified transformations
5. Scalability of the system
6. Version control and change management
7. Enabling collaboration
8. Multi-database platform
9. Security and Access Control
10. Monitoring and alerts

Why is automation important with Data Vault?

Automating your data warehouse implementation is crucial with Data Vault methodology

Before we launch into the list, it's important to mention automation. Because Data Vault standardizes data structures and processes, this makes it easier to automate various labor-intensive tasks, including but not limited to:

• ELT (Extract, Load, Transform) tasks
• Metadata-driven development
• Model generation

Data Vault's consistent and modular approach separates business keys, relationships, and context, which makes it highly pattern based and enables load processes automation. With automation you can reduce manual coding and enable more scalable, replicable processes. With automation since it reduces manual coding and enables scalable, replicable processes. 

This means faster development, reduced errors, and easier maintenance.

Agile Data Engine’s out-of-the-box functionality supports full Data Vault implementation and automation

1 Data Modeling Capabilities

Agile Data Engine streamlines the process of design, deployment and management of Data Vault model objects.  To facilitate this, data modeling and data transformation functionality is kept on the same platform. This enables seamless continuous delivery for the data warehouse solutions and helps keep data modeling and transformation better in sync with each other.

Data Vault is about modeling data on a conceptual and logical level. With Agile Data Engine, you can design your conceptual business entities and logical data vault model and generate physical data model based on logical metadata. Agile Data Engine fully automates the physical data model creation and ensures the consistency of the data model schema changes.

You can also easily navigate large and complicated Data Vault models with a visual data model graph, with the flexibility in browsing and filtering of the data model content based on your needs.

Figure 1: Data models can become very complicated quickly. Agile Data Engine helps you to manage the complexity.

2 Support for core Data Vault object types

Agile Data Engine supports the full spectrum of core Data Vault 2.0 object types. The creation of common entities such as Hubs, Links, Satellites, Effectivity and Status satellites, Multi-active satellites, Point-in-time (PIT) tables and Non-historized link tables is fully automated.  There are templates for designing and implementing these object types and you can also configure your own customized object types with logical entity type functionality.

Data modeling with Agile Data Engine is based on so-called logical entity types that work like templates in the modeling process. You can define your own data modeling standards using entity type templates. With Agile Data Engine, you can automate the creation of Data Vault entity types and their metadata fields and for example entity naming standards.

As part of data modeling automation, ADE helps in utilization of data vault structures, by creating database views showing the latest versions of data. This way the view creation scripts are created always in a consistent way.

Figure 2: Agile Data Engine automates the data vault implementation, both modeling and transformation, with it’s metadata driven approach. For example, the naming conventions and all the needed metadata columns will be automatically generated.

3 Automation of Data Warehouse engineering

Besides data modeling, Agile Data Engine helps in automating also other areas of work in the data warehouse engineering process.

Agile Data Engine generates the data transformations needed for loading the Data Vault objects. You need to just map the source and target tables together, and Agile Data Engine will generate the physical SQL transformation scripts.

The heart of Agile Data Engine is a built-in CI/CD pipeline for data warehousing, enabling the Continuous delivery out of the box. You don’t need to build and maintain your own customized CI/CD system, and you can focus on solving the data and business requirements. Automated continuous delivery process enables small iterations.

The starting point in continuous delivery is small and frequent deployments. It is all about repetition and getting fast feedback for your work. By following continuous delivery, you will teach your whole system to handle the changes efficiently and gracefully.

CI/CD pipeline handles database schema changes and managing data load workflows automatically, without need for maintaining your own schema change or data flow generation scripts. ADE handles the changes on the background, even while the data loads are running. So you don’t need to worry about continuous breaks in the service to your business stakeholders.

Figure 3: With Agile Data Engine’s advanced dependencies management, you automate the audit trail and impact lineage requirements.

4 Reusability and unified transformations

With Agile Data Engine, you can develop reusable business logic and calculations for example for handling business keys, surrogate keys, relationships between entities, technical fields, unstructured types. Automated design patterns defined on logical level ensure the consistency and coherence in your data warehouse – impacting on better productivity and data quality.

However, you are not locked only with predefined patterns and standards, as it is easy to define customer specific transformation logic as well, and you can further develop your own reusable patterns and standards.

Automation forces standardization to the data engineering process which results in development and operations efficiency, and consistency across the whole environment.

5 System scalability

Data Vault and Agile Data Engine together enable the design of scalability into your data warehouse. This covers both horizontal scalability (number of integrations) and vertical scalability (data volumes).

As volume of data and complexity of the system grows, Agile Data Engine helps in the scaling the data processing capabilities with the flexibility to control full or delta loads, with it’s built-in features allowing optimal delta processing throughout your data warehouse.

Standardization of modeling and engineering patterns has a key role in ensuring the scalability of the data warehouse over its lifecycle. It is inevitable that there will be changes in data team, as people are leaving and new are coming in. It is easy to end up in a situation that there are high risk person dependencies, if only one member in the team understands how a solution is built and is the only one capable for maintaining it. Agile Data Engine and Data Vault both safeguard you from these person locks from happening. 

Implementing a data warehouse with Agile Data Engine, is like building the system with LEGO bricks. ADE modularizes data warehouse implementation based on its metadata concepts, like entities and packages, and multiple automation features. Modularity is important for implementing a data as a product approach with your data warehouse.

6 Version control and change management

With Agile Data Engine’s metadata-driven approach, you get the full auditability of the data warehouse models and data. You have visibility to all the metadata changes and you can see who has done the changes. Agile Data Engine has a centralized metadata repository and built-in version control, so you can compare before and after the change situations, and go back to previous status of the system. 

The out-of-the-box CI/CD pipeline and version management implements a clear environments separation for your runtime environments (for example development, testing, production) that lowers the risk of breaking the solutions running already in production.

7 Enabling collaboration

Agile Data Engine has a shared development repository, so it is easy to collaborate on the whole data warehouse content within the data team. It supports collaborative modeling, development, and concurrent work on Data Vault entities. Agile Data Engine provides guard rails for the data team (or multiple teams), and deployment and promotion of the deployments does not lead to collisions.

You can (and should) also document your system using Agile Data Engine’s metadata capabilities. This way your solution documentation is always up to date, in real time, and anyone can continue anyone else’s work and troubleshoot possible problems.

8 Multi-database platform

Agile Data Engine supports all the major cloud database platforms, like Snowflake, Databricks SQL, Azure Fabric, Google BigQuery and Amazon Redshift. Same Data Vault features work in all these database platforms. This means that the design and Data Vault implementation effort you have done with some of these platforms, is portable across various database engines

9 Security and Access Control

Data Vault helps ensure security at a granular level through its high normalization approach. Agile Data Engine provides functionality to maintain role-based access control for database entities.

Auditing capabilities will help ensure data privacy requirements and compliance with regulatory requirements.

10 Monitoring and alerts

Using the Data Vault methodology increases the amount of individual data loads and complexity of the data load workflows. Agile Data Engine helps to manage the complexity with its monitoring capabilities. 

Agile Data Engine enables continuous data quality measurement with it’s smoke testing functionality. Everything you can do with SQL, can be used for implementing the data tests. This powerful feature combined with Data Vault’s innate practices help to monitor and improve data quality, and so increase the trust to the data.

Agile Data Engine and Data Vault

ADE was built for automating cloud-based data warehouse implementations, including strong support for Data Vault 2.0

When we built ADE, we wanted to merge proven data management practices with the latest technological capabilities. When we were building our product, the Agile Data Engine team received insights and guidance from some of the most experienced Data Vault experts, which we incorporated when designing the product features.

Just how popular is Data Vault? Well, 70 % of Agile Data Engine customers use Data Vault as part of their data warehouse data architecture. They've met success with:

• Complex environments
• Large data volumes
• Business real-time data refreshing.
  

Our customers are very happy with the results, and especially like how Agile Data Engine offers rich functionality for implementing and operating Data Vault models in a cloud data warehouse natively out of the box. 

This means all features and benefits work across the common database platforms that ADE currently supports, without any need for acquiring and integrating external plug-ins.

Workshops and Trainings

If you're curious to learn how to avoid or overcome common Data Vault challenges, check out our training with in-house data modeling expert Tevje Olin.

We also offer a workshop focused on best practices for building and maintaining a resilient data warehouse. Whether you're planning to migrate your data warehouse to the cloud, build a new one from scratch, or face stability issues and challenges with maintaining and developing your current data warehouse, you will develop valuable ideas during the session.