What is Data Vault?
| @intelia | July 7
What is Data Vault?
This post provides an overview of Data Vault including benefits, pre-requisites when considering implementing, primary high level implementation steps and also challenges to be aware of. Developed by Dan Linstedt in the early 2000s as a response to limitations and challenges of traditional data warehousing techniques, Data Vault is a data modelling and data warehousing methodology aimed at providing a flexible and scalable approach to storing and managing enterprise data.
Key Data Vault principles include:
- Hubs: Represent business entities or core concepts and serve as the primary keys for storing information about them.
- Links: Define relationships between hubs and represent the associations or interactions between business entities. They act as bridges between hubs and enable the capture of complex relationships.
- Satellites: Contain descriptive attributes and additional information related to hubs and links. They store historical data, timestamps, and other context-specific details.
The Data Vault methodology aims to provide a flexible, scalable, and auditable foundation for data integration and analysis. The focus is on storing raw, granular data in its native format in the raw data vault and transformed, cleansed, and integrated business data in the business data vault. The raw data vault records raw, historical data that can be easily traced back to source systems for data lineage and auditability purposes. The business data vault aims to integrate and standardise the raw data across multiple, independent source systems and provide a central location for conformed and transformed business data that can be consistently consumed across the enterprise. This approach enables agility and adaptability, allowing for easier integration of new data sources and changes in business requirements, therefore making it well-suited for complex and evolving data environments.
Data Vault Benefits
Scalability and Flexibility
Designed to handle large and complex data integration scenarios, it provides a flexible data modelling approach that allows for easy adaptability to changing business requirements and evolving data sources. As data volumes and sources grow, Data Vault can scale and accommodate new data without requiring significant modifications to the existing structure.
Data Traceability and Auditing
Incorporating the structured concepts of “Hub,” “Link,” and “Satellite” tables to represent the business entities, relationships, and attributes in a standardised manner ensures traceability of data back to its source, providing a historical record of changes and allowing for detailed auditing and compliance requirements. It enables businesses to track data lineage and maintain data integrity.
Incremental Data Loading
Data Vault supports incremental loading, meaning only changed or newly added data needs to be processed during the typical ETL (Extract, Transform, Load) ingestion process, which reduces time and resources required for data integration by eliminating the need to process and load an entire dataset each time. Incremental loading supports near real-time data updates, enabling businesses to have more up-to-date information for analysis. Finally, an insert only approach is used when loading records which means that all business data is only ever inserted and never updated leading to high-speed loading of the source data into data vault.
Scalable Performance
From an architecture perspective, Data Vault is optimised for parallel processing, making it suitable for handling large volumes of data. By distributing the workload across multiple nodes or processors, Data Vault can achieve high performance and faster query execution times. This is particularly beneficial when dealing with complex joins and aggregations across multiple tables.
Data Quality and Consistency
Data consistency and accuracy is ensured through separation of business rules and validation logic from the raw data vault. By centralising the data integration and transformation processes, the business data vault ensures cleaned and conformed business data is available to all downstream consuming applications in a business consistent form.
Agility and Adaptability
Data Vault’s flexible modelling approach allows for easy integration of new data sources, accommodating changes in business requirements, and supporting iterative development. It enables businesses to quickly onboard new data, incorporate additional business entities, and adapt to evolving analytics needs, which is especially valuable in dynamic business environments where data requirements change frequently.
Collaborative Data Sharing
By providing a common language for data integration, along with the standardised structure and clear relationships between tables, data sharing and collaboration across teams and departments is much easier, and it’s also easier for different stakeholders to understand and interpret the data. Overall, this promotes stronger collaboration and data-driven decision-making, and encourages a data-driven culture within an organisation.
Pre-requisites to implementing Data Vault
Data Strategy
A clear data strategy is essential before implementing a Data Vault. An organisation must be clear in defining the goals and objectives of the data initiative, including identifying business requirements, and determining the scope of the Data Vault implementation. The data strategy must align with the overall business strategy and provide a roadmap for data integration, analytics, and governance.
Data Governance
Establishing a robust data governance framework is crucial for a successful Data Vault implementation, including defining data ownership, roles, and responsibilities, establishing data quality standards, and implementing data governance processes. Data governance ensures that data is managed consistently, conforms to defined standards, and is governed throughout its lifecycle.
Data Modelling Skills
Data Vault requires strong understanding and capability in data modelling principles and techniques. Organisations must have skilled data modellers who can design and implement the Data Vault schema correctly. These people must have strong experience and expertise in modelling Hubs, Links, and Satellites and must understand the relationships and business rules associated with an organisation’s data entities.
Data Integration Tools
Selecting appropriate data integration tools is crucial for implementing a Data Vault. These tools should support the extraction, loading and transformation (ELT) processes required to populate the Data Vault. Look for tools that provide features such as change data capture, incremental loading, data cleansing, and transformation capabilities.
Source System Analysis
Conduct a thorough analysis of the source systems that will provide data to the Data Vault. Understand the data structures, relationships, and data quality issues in the source systems. This analysis will help in designing the Data Vault schema and planning the data extraction and transformation processes.
Data Quality and Cleansing
Data quality plays a significant role in the success of a Data Vault implementation so organisations must ensure that data quality issues in source systems are able to be identified and addressed. Data teams should establish data quality metrics and monitoring mechanisms to continuously monitor and improve data quality, and may also need to consider implementing data cleansing and transformation processes to resolve / improve data quality issues before loading it into the Data Vault.
Change Management
Implementing a Data Vault involves changes to data integration processes, data management practices, and analytical workflows. It is important to have a change management plan in place to address the organisational impact of the Data Vault implementation and also to communicate the benefits of the approach to key data eco-system stakeholders, provide training, and address any concerns or resistance to change.
Technology Infrastructure
Data teams must evaluate the technology infrastructure required to support the Data Vault implementation, including selecting a suitable data warehousing platform, ensuring sufficient storage and compute resources, and considering scalability requirements. Modern cloud-based data platforms are often best-suited to Data Vault due to their scalability and flexibility.
Skills and Expertise
Building and maintaining a Data Vault requires a team with the right level of skill, experience and expertise. Organisations must ensure that the Data team have a strong understanding of data integration, data modelling, ETL processes, and data analytics, and should be upskilled as needed.
Data Vault Challenges
Data Vault does have its challenges, particularly associated with implementation and maintenance. It’s fair to say most of them can typically be overcome with proper planning, design, and implementation, but, regardless, here are a few common ones organisations need to be aware of:
Complexity
Data Vault modelling can be complex and requires a thorough understanding of the methodology. Designing and implementing a Data Vault architecture typically involves a steep learning curve and requires a different design approach to traditional data modelling approaches and requires organisations to have experienced resources with appropriate knowledge and expertise.
Large Data Volume
Data Vault emphasises storing raw, granular data and, as a result, volume of data can increase significantly which can lead to high storage requirements with increased processing overhead, particularly when dealing with large-scale data sets. However, when using modern Cloud-based data platform solutions, the cost of storage is typically relatively low and therefore this challenge is often readily mitigated.
Performance Considerations
The nature of Data Vault, with its multiple tables and relationships, can impact query performance. Complex join operations across the different tables may result in slower query response times unless proper indexing and tuning strategies are implemented. Specialised data vault structures are required to reduce the complexity of end user queries.
Data Quality and Consistency
Oftentimes, raw data ingested contains quality issues (inconsistencies or errors). While Data Vault provides traceability and auditing capabilities, ensuring data quality and consistency can still be a challenge, especially when dealing with diverse and numerous data sources. The preferred approach is wherever possible make the source systems responsible for providing high quality data and minimise the need for data vault to improve any poor-quality source data.
Modelling Complexity
Designing an effective Data Vault model requires careful consideration of business requirements, relationships between entities, and the granularity of data. Poorly designed Data Vault models can lead to difficulties in data integration, maintainability, data analysis and overall “speed to insights” (ie: the time taken to get data into your data analytics platform and making it available for consumption by business data users).
Development and Maintenance Overhead
Implementing and maintaining a Data Vault often requires additional effort and resources when compared directly to more traditional data warehousing approaches. The creation of hubs, links, and satellites, as well as the associated business rules and transformations, can add complexity to the development and maintenance process, and requires organisations to have the right number of experienced resources with appropriate knowledge and expertise. For this reason, it is important for the data vault to be designed using standard practices and principles to minimise complexity and also enhance performance through automation.
Tooling and Ecosystem Support
Whilst Data Vault has gained popularity, the availability of tools and ecosystem support specific to Data Vault modelling and management may be limited when compared to more traditional data warehousing approaches leading to organisations potentially needing to invest in specialised tools or adapt existing tools to support their Data Vault implementation.
Implementing Data Vault
Typical high-level steps and considerations for Data teams to successfully implement Data Vault include the following:
Initial Planning and Preparation
The Data team should ensure they upskill to gain a thorough understanding of Data Vault’s methodology, architecture, and model(s). The Data Vault Alliance website contains comprehensive information and resources which can assist in this process.
Define Business Requirements
It is imperative to understand, agree and document key business requirements, drivers, objectives, opportunities, problems, pain points, challenges etc that are driving the need for a Data Vault implementation in order to create business value, resolve identified challenges and / or enable identified opportunities.
Identify Data Sources
The next step is identifying relevant high value data sources that will provide input to the Data Vault, including (internal and external) databases, files, APIs, and other sources of structured and unstructured data. One way to achieve this is to conduct a comprehensive analysis of systems and data used by business users to help perform their role (eg. used for reporting, analytics, dashboards, advanced analytics, decision-making etc) and identify high value data that will provide input to the Data Vault. For each high value data source, it is important to understand data structures, relationships, and data quality issues. Thorough source system analysis ensures accurate and reliable data integration into the Data Vault. This analysis provides critical input into, and will guide, the design of the Data Vault schema as well as help identify any data transformations or cleansing activities that may be required up front.
Design the Data Vault Model
The Data Vault model then needs to be carefully designed, consisting of hubs, links, and satellites, along with defining the primary keys, attributes, relationships between the entities, the level of granularity and how data will be partitioned in the Data Vault in order to meet / resolve / enable key agreed business requirements, drivers, objectives, opportunities, problems, pain points, challenges etc. When designing the Data Vault model, it is often helpful for teams to think in terms of “speed to insights” as much as possible (ie: reducing the time taken to get data into the data analytics platform and making it available for consumption by business data users). By having this mindset, teams can hopefully find the right balance between “theoretical model perfection”, which may be inflexible and substantially complex to implement and maintain, against “a robust and flexible working technical solution” that creates real business value in a timely manner.
Establish Data Governance
Taking identified data sources into account, Data governance practices and policies should be defined to ensure data quality, consistency, and security within the Data Vault, including establishing rules for data entry, updates, and deletion, as well as guidelines for data lineage, auditing, privacy and compliance.
Getting Data into the Data Vault
The best approach is Extract, Load, and Transform (ELT), where data is loaded into the raw data vault first and transformation is performed later in the business data vault as required.
However, data teams should be aware of / take into consideration (based on their knowledge of their business data), whether any data transformation, cleansing, and mapping in the source systems is required to ensure quality and fit against the Data Vault structure. Using appropriate data integration tools, relevant data is extracted from each identified source based on defined criteria and ingested into the data platform. It’s common to stage or land ingested source data in a temporary storage area before it is loaded into the Data Vault which provides an historical store of integrated business data ready for consumption by downstream users and applications. Data teams should begin by loading data into the hub tables, ensuring that unique identifiers or natural keys for each entity are appropriately assigned or mapped to corresponding hubs. Link tables should be loaded next, capturing relationships between entities, and associating them with relevant hubs. Finally, satellite tables should be loaded, and populated with descriptive attributes and additional information related to hubs and links. Satellites should capture historical data, timestamps, and context-specific details, and it is crucial they are properly linked to hubs and links.
Enable Data Lineage and Traceability
Data teams should ensure that data lineage and traceability are maintained throughout the Data Vault ecosystem and should establish mechanisms to track the origins and transformations of data, enabling comprehensive auditing, compliance, and troubleshooting capabilities. Data lineage provides transparency and confidence in the data flowing through the Data Vault, enhancing trust and decision-making.
Implement Security Measures
Robust security measures should be implemented to safeguard data within the Data Vault. In order to achieve this, Data teams should define access controls, authentication mechanisms, and encryption protocols to protect sensitive data, and comply with relevant data protection regulations and industry standards to ensure the confidentiality and integrity of the data stored in the Data Vault.
Build Business Intelligence (BI) Layer
Data teams can then create an appropriate business intelligence layer on top of the Data Vault to enable data analysis and reporting. This layer can include data marts, dimensional models, or other structures that facilitate data consumption by business data users.
Automate as Much as Possible
Data teams should seriously consider implementing automation tools and processes to streamline the end-to-end Data Vault implementation, including data ingestion / integration, data transformation, and any common and repetitive tasks, therefore improving efficiency, reducing manual effort (also called TOIL), and reducing risk of human error.
Monitor, Maintain, Iterate and Enhance
Like any critical system, Data Vault must be continuously monitored and maintained to ensure its integrity, performance, and alignment with evolving business requirements.
Data teams should institute data quality checks, perform regular data purges, continue to seek and action feedback from business data users on how to improve the effectiveness of the solution, and constantly refine and enhance the Data Vault model, processes, and governance practices to accommodate new data sources or changes in the business landscape in line with evolving needs of the business.
Conclusion
Implementing Data Vault can revolutionise the way an organisation integrates and analyses data, providing a scalable and flexible foundation for data-driven decision-making. However, it is not an insignificant undertaking and organisations must carefully and thoroughly plan to maximise the chance of a successful implementation to enable efficient data integration, enhanced data quality, and robust analytics capabilities, unlocking the full potential of data assets in support of business outcomes.