Over the past few years, modern data technologies have been allowing businesses to build data platforms of increasing complexity, serving ever more sophisticated operational and analytical needs. This introduces a need for effectively managing that complexity at scale – enter Data Vault, a holistic framework for creating and scaling an enterprise-grade data warehouse. Read on to understand its benefits, and how to get started building a Data Vault for your business using two popular technologies, Snowflake and dbt Cloud.
What Is Data Vault 2.0?
Data Vault 2.0 is an agile, scalable framework for designing, building and maintaining modern data warehousing systems. It gels well with domain-oriented frameworks such as data mesh, but can be applied successfully in a variety of scenarios.
Originally just a data modelling methodology, ‘Data Vault’ became ‘Data Vault 2.0’ to reflect a more holistic framework for the whole data warehouse solution – including reference architecture, development and operational processes, agile project delivery, automation and continuous improvement.
Solving complex issues, such as agility, scalability, flexibility, auditability and consistency, Data Vault 2.0 sets out standards and guidelines to build a scalable data warehouse.
It features a hybrid approach that combines the best of traditional data modelling frameworks such as 3NF and Star Schema, which results in a flexible and easily extensible data model. It also emphasises the use of automation and ELT processes to provide faster implementation whilst allowing for robust data governance (Data Architecture: A Primer for the Data Scientist, William H. Inmon and Daniel Linstedt).
What are the benefits of Data Vault 2.0?
Data Vault 2.0 is often referred to as a single source of fact because it integrates source data, keeps the original data intact and stores historical changes as well. The model offers several advantages over traditional data warehouse models:
- Scalability: The Data Vault 2.0 model is highly scalable, allowing for easy implementation of new data sources and the ability to handle massive amounts of data effectively. Decoupled architecture and a business-focusing approach let you integrate entities and connections without reengineering or breaking processes;
- Flexibility: The modular structure of the framework enables organisations to adapt and modify the data warehouse as business requirements change, without causing any disruption or requiring extensive rework;
- Agility: You can start by modelling only one part of your system and build incrementally. With minimal dependencies on other components, Data Vault 2.0 makes it easier to build, maintain and modify the data warehouse over time;
- Improved data quality: The separation of data into hubs, links, and satellites ensures better data quality by minimising data redundancy, promoting data consistency and isolating potential data integrity issues;
- Auditability and compliance: The model makes it straightforward to implement audit and compliance requirements, as it maintains a complete history of changes to the data, along with the load date and record source information. This is great for environments that deal with GDPR, HIPAA, PII or CCPA, because you can demonstrate when the information was loaded and what the source was;
- Historical tracking: Data Vault 2.0 maintains a history of changes to the data, providing valuable insights into historical trends and enabling accurate analyses of business changes over time;
- Near real-time and parallel loading: The modular nature of Data Vault 2.0 allows for parallel loading of data, resulting in faster data warehouse loading and reduced load times.
Although the framework is not industry-specific, these features make it particularly beneficial for complex or highly regulated environments such as healthcare or insurance. It also enables teams that have to deal with multiple, changing data sources to handle these whilst remaining productive. Need to build a single historical view over three different CRMs that your company had? Data Vault is designed for that too.
Data Vault components
The core components of Data Vault are hubs, links and satellites. They allow for more flexibility and extensibility, and can be used to model complex processes in an agile way.
The main components of Data Vault 2.0 are:
- Hubs: A hub is essentially a grouping of related business concepts. For example, if you were storing data about customers, you might have a "customer" hub that contains information about each individual customer (e.g. name, address, phone number, etc.);
- Links: Links connect your hubs in Dat Vault. For example, if you have a table that contains information about customers and a table that contains information about orders, you might use a link to connect a customer to an order they placed;
- Satellites: The satellites are attachments to the main entity and contain additional information that can change over time, like a history of updates to the entity. For example, a satellite might contain descriptive information about products, such as a name, colour options and price.
Think of components as LEGO bricks: they are modular and you can combine them in many different ways to build a wide variety of different, cohesive structures.
How to succeed with Data Vault implementation
Any successful Data Vault project starts with a clear set of business objectives and requirements. These need to be translated into a conceptual data model, which then forms the foundation for an implementation roadmap. Infinite Lambda can guide you through this planning process through a series of structured workshops, and provide best practices and automation capabilities throughout
Tooling and architecture are critical to the success of a Data Vault project. Cloud-based data technologies such as Snowflake and dbt Cloud provide the flexibility and scalability needed, and make it easy to apply data protection rules, create documentation and foster collaboration. We have implemented several successful Data Vault projects using Snowflake and dbt Cloud on behalf of clients in a number of industries.
Large scale Data Vault implementation
Implementing a Data Vault at scale can be a challenge. At Infinite Lambda, we have developed in-house methodologies and tools to help manage large-scale implementations. We typically recommend dbt Cloud, as its modular approach allows rapid development and provides visibility on each layer of data transformation via its native lineage tool.
Simple, template-based components facilitate automatisation and code generation. You avoid the boring stuff, boost productivity and remove the manual errors from the implementation process.
Modern data warehousing technologies such as Snowflake are optimised for high performance with Data Vault models, and give you full visibility across data pipelines all the way to the reporting layer.
Look out for the next post in this series, which will dive deeper into implementation best practices.
You can start getting value out of Data Vault before finishing the full implementation. We typically recommend a domain-oriented approach, choosing one domain to implement first, before moving on to others. This allows operational and analytical use cases to be unlocked incrementally. This works well whether or not you are following a Data Mesh style of architecture.
Documentation is also crucial to adoption, because it allows people across the business to quickly understand the data assets that you have created. dbt Cloud has native documentation capabilities that are dynamically linked to the models you build, making it a great choice for Data Vault.
Data Vault with dbt on Snowflake
For organisations that are already on Snowflake or are looking to migrate to it, implementing Data Vault 2.0 together with dbt can empower them to effectively integrate data from various sources, increase productivity, enhance analytics and reporting capabilities, and foster collaboration among team members.
This powerful combination of tools and methodologies enables data-driven decisions and improves operational efficiency, so that businesses can stay ahead in an increasingly competitive landscape.
Data Vault 2.0 is a powerful data modelling approach that provides flexibility, scalability and maintainability for modern data warehouse implementations.
By leveraging the building blocks of Data Vault, organisations can build data warehouses that are adaptable to changing business requirements, promote data quality and integrity, and enable efficient data management and analytics. This in turn drives better decision-making, competitive advantage and business growth.
Choosing the right methodology for building your data warehouse is crucial for your system’s capabilities in the long run. If you are exploring Data Vault and want to learn more, we can help you make the right call for your organisation.
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See how Infinite Lambda leveraged Data Vault with automations on an advanced analytics project.