What are the advantages of using Data Vault modeling in data architecture?

Data Vault modeling is a data architecture technique that aims to create a scalable, flexible, and reliable data warehouse. It is based on the principles of separating business keys, attributes, and relationships, and storing them in different types of tables. In this article, you will learn about the advantages of using Data Vault modeling in data architecture, and how it can help you deal with complex and changing data sources.

1 What is Data Vault modeling?

Data Vault modeling is a data modeling method that was developed by Dan Linstedt in the late 1990s. It is designed to handle the challenges of data integration, data quality, data governance, and data history in a data warehouse. Data Vault modeling consists of three types of tables: hubs, links, and satellites. Hubs store the unique business keys that identify the entities or concepts in the data. Links store the associations or relationships between the hubs. Satellites store the descriptive attributes or details of the hubs or links, along with timestamps and metadata.

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Anant Arora

Data Engineering, Analytics and AI Leader - Snowflake, Databricks, Data Fabric, Bigdata
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It offers unparalleled benefits: Flexibility and Scalability: Easily adapt to changing data needs without disruption. Data Governance and Traceability: Ensure trust and accountability through transparent data lineage. Data Integration: Seamlessly combine data from diverse sources, enabling better decision-making. Historical Data Retention: Preserve valuable historical data for insights and improvements. It's not just about data; it's about staying agile and innovative in today's fast-paced world.

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk, increasing agility, realizing strategies, and driving innovation.
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Data Vault’s hub-link-satellite structure flexibly captures evolving enterprise data. The use of business keys, in hub tables allows for integration across sources. Satellite tables are designed to store information while isolating any changes that occur. Links map relationships across time. Additionally, the persistent staging area provides support for analytics. It's worth mentioning that governance and lineage are features of Data Vault making it an ideal choice, for data-driven businesses.

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2 How does Data Vault modeling improve scalability?

One of the advantages of Data Vault modeling is that it allows for high scalability and performance. Because the tables are normalized and have simple structures, they can be easily partitioned, distributed, and parallelized across multiple servers or nodes. This reduces the load and contention on the database, and enables faster data loading and querying. Data Vault modeling also supports incremental loading and delta detection, which means that only the new or changed data is loaded into the data warehouse, without affecting the existing data.

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3 How does Data Vault modeling enhance flexibility?

Another advantage of Data Vault modeling is that it provides flexibility and adaptability to changing business requirements and data sources. Because the hubs and links are stable and independent of the attributes, they can accommodate new or modified data without affecting the existing data model. For example, if a new source system is added, or a new attribute is discovered, it can be simply appended as a new satellite to the corresponding hub or link, without altering the existing tables or relationships. This minimizes the impact and cost of changes, and preserves the data history and lineage.

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Davood Geravand

Business Intelligence Developer | SQL | ETL | DWH | Python | Azure
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One of the main differences between Data Vault and other forms of design is that from the beginning, we maintain the connections between the HUB tables in the LINK tables in "many-to-many" relationships. And this makes us very flexible in the face of business changes. Also, you don't need to make your Enterprise DWH, which includes Data Vault, available to all users. You can implement an appropriate Information Vault in dimensional model for users outside the development team.

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4 How does Data Vault modeling ensure reliability?

A third advantage of Data Vault modeling is that it ensures reliability and quality of the data. Because the data is loaded as it is, without any transformation or manipulation, it preserves the original source data and its integrity. Any errors, inconsistencies, or anomalies in the source data are captured and flagged in the satellites, and can be resolved later in the data warehouse or downstream systems. Data Vault modeling also enables auditing and tracking of the data, as each satellite records the source, time, and metadata of the data.

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5 How does Data Vault modeling support data governance?

A final advantage of Data Vault modeling is that it supports data governance and compliance. Because the data is stored in a standardized and consistent way, it can be easily documented and understood by the data consumers and stakeholders. Data Vault modeling also facilitates data security and privacy, as the sensitive or confidential data can be isolated and encrypted in the satellites, and accessed only by authorized users or applications. Data Vault modeling also complies with the regulations and standards of data management, such as GDPR, ISO, and DAMA.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Davood Geravand

Business Intelligence Developer | SQL | ETL | DWH | Python | Azure
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pros: + Flexible and adaptable to meet the changing needs + Supports incremental delivery + Integration with big data technologies + Changes does not impact existing structure cons: - More table requires lot of joins and impractical for direct querying - Load data marts can be rather complex

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Anand Kumar

Principal / Enterprise Architect
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Data Vault - Data Audit & Security purpose Before using the Data Vault solution, it is important to understand which data needs to be Audited and Secured. There are multiple ways to secure the data at storage or DB level. My experience is that Data Vault Solutions are important for Financial Transactions, sensitive data recorders in Telecom and manufacturing. For defence or government, it's a different call. For eCommerce, CRM, ERP, retail ... DB level security is more important as these are short duration transactional data (mostly non financial) which can be achieved through various other means (crypto, HSM, script, DB encryption...) For large projects having financial and sensitive data transactions, Data Vault is advisable.

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What are the advantages of using Data Vault modeling in data architecture?

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1 What is Data Vault modeling?

2 How does Data Vault modeling improve scalability?

3 How does Data Vault modeling enhance flexibility?

4 How does Data Vault modeling ensure reliability?

5 How does Data Vault modeling support data governance?

6 Here’s what else to consider

Data Architecture

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What are the advantages of using Data Vault modeling in data architecture?

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3

4

5

6

1 What is Data Vault modeling?

2 How does Data Vault modeling improve scalability?

3 How does Data Vault modeling enhance flexibility?

4 How does Data Vault modeling ensure reliability?

5 How does Data Vault modeling support data governance?

6 Here’s what else to consider

Data Architecture

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