Data Mesh vs Data Vault: Key Differences, Practical Examples, Use Cases & What Suits Your Business
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Data mesh is a new architectural paradigm that treats data as a product. On the other hand, a data vault is a specific type of data modeling methodology for designing agile, scalable data warehouses.
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In this blog, we will learn how both data mesh and data vaults are two very different concepts, serving different purposes within the data architecture.
Table of contents
- Data mesh vs data vault: Navigating through the data maze
- Steps to consider before implementing data mesh or data vault
- Data mesh vs data vault: Practical examples and use cases
- Navigating the depths: Unraveling data mesh and data vault
- Comparing data mesh and data vault: Unveiling the contrasts in a comparative table
- Bringing it all together
- Data mesh vs data vault: Related reads
Data mesh vs data vault: Navigating through the data maze
Let us begin with a brief comparison:
What is a data mesh?
Data mesh is a decentralization of data domains where each domain is treated as a full product, owned by a cross-functional team that has data product owners, engineers, and analysts. It was created to address the complexities that arise from scaling data, especially in large organizations.
The core principles of data mesh include:
- Domain-oriented decentralized data ownership and architecture
- Data as a product
- Self-serve data infrastructure as a platform
- Federated computational governance
The main goal of data mesh is to democratize data and allow for better data governance and compliance. It is especially applicable to organizations that have massive amounts of data spread across multiple domains or teams, and where the monolithic, centralized data lake or warehouse has become a bottleneck.
What is a data vault?
Data vault is based on the principles of flexibility, scalability, and adaptability, which makes it particularly well-suited for dealing with large, complex data environments, or rapidly changing systems.
Data vault consists of three types of tables: Hubs, Links, and Satellites.
- Hubs represent business keys or unique list of values
- Links represent the associations or relationships between the Hubs
- Satellites hold descriptive data or context about the Hubs or Links
Steps to consider before implementing data mesh or data vault
To understand the differences and how they can be applied to your business, here are a few steps to consider:
- Assess your current state
- Define your goals
- Understand the concepts
- Consult with experts
- Run a pilot
Now, let us look into each of the above steps in brief:
- Assess your current state
Start by taking a deep dive into your current architecture, understanding where the data is coming from, who is using it, and how it is being used.
- Define your goals
Identify your data goals. This could include things like speeding up the delivery of insights, reducing costs, ensuring data security and privacy, or scaling your data infrastructure.
- Understand the concepts
Dive deep into both Data Mesh and Data Vault concepts. Research online resources, read case studies, and consult with peers in the industry.
- Consult with experts
Engage with experts who have implemented both Data Mesh and Data Vault. You can also attend conferences or webinars on these topics.
- Run a pilot
If feasible, consider running a pilot on a small scale to understand the practical implications of implementing each approach.
Remember, these are not mutually exclusive concepts. In some cases, you might find that a combination of data mesh and data vault could serve your needs better. It’s important to ensure that the approach you choose aligns with your overall business strategy and goals.
Data mesh vs data vault: Practical examples and use cases
Now, let us better understand the use cases for data mesh and data vault using practical examples.
Data mesh use cases
- Consider a large multinational bank. They have departments like retail banking, corporate banking, finance, risk management, compliance, etc.
- Each of these departments has its own set of applications and generates massive amounts of data that needs to be processed and analyzed.
- In the traditional centralized data architecture approach, all this data would be pulled into a central data lake or warehouse.
- But as the data grows, managing this monolith becomes increasingly complex and unwieldy. Data may be stale by the time it’s ingested and processed.
- Furthermore, each department might have unique data needs that the centralized model doesn’t cater to effectively.
Enter data mesh. In a data mesh architecture, each department would become a data product owner. They are responsible for their data from creation to consumption, ensuring its quality, timeliness, and relevance.
- The retail banking department can handle its own data related to account transactions, customer profiles, loan data, etc.
- Similarly, the risk management department handles data about credit risk, market risk, operational risk, etc.
- Each of these domains can use the technology stack that best suits its needs. Yet, they can adhere to the company-wide data platform standards and interfaces, fostering innovation while ensuring interoperability.
Data vault use cases
- Now let’s consider a telecom company. Over the years, they have merged with or acquired several other companies, each with its own IT systems and data formats.
- They need a single source of truth, but given the complexity of their data landscape, traditional data warehousing methods aren’t flexible or adaptable enough.
This is where the data vault shines. Using data vault modeling, they can create a scalable data warehouse that can easily adapt to changes.
- Each company’s unique identifiers for customers, services, etc., can be represented as Hubs.
- The relationships between them, perhaps a customer subscribing to a service, can be represented as Links.
- Additional information about customers or services, such as a customer’s address or a service’s pricing, can be represented as satellites.
- As the telecom company acquires a new company, new hubs, links, and satellites can be added to the data vault without disturbing the existing structure. If a new source system provides additional information about a customer, a new satellite can be added to the existing customer hub.
So, in summary, data mesh is a great fit for organizations dealing with large-scale, domain-diverse data that want to democratize data ownership and processing.
On the other hand, data vault is a robust solution for companies dealing with complex, evolving data landscapes that need a flexible, adaptable data warehousing solution.
Navigating the depths: Unraveling data mesh and data vault
Now that we know the basics of data mesh and data vault, let’s go a bit deeper into these concepts:
Additional factors to keep in kind for data mesh
1. Cultural shift
One of the biggest challenges with data mesh isn’t the technology but the cultural and organizational change. It requires a shift from centralized data teams to decentralized domain-oriented data product teams.
This means changing how people work, think about data, and even how they’re organized and rewarded.
2. Data product thinking
Data mesh requires thinking about data as a product, which means data must provide value to its consumers. This includes aspects like data quality, data freshness, data discoverability, and data security.
3. Platform teams
While data ownership is decentralized in a data mesh, a centralized team typically still exists. But instead of owning all data, they are responsible for providing the data infrastructure platform that the data product teams use.
This could include data storage, data processing, data observability, and data security tooling.
4. Cross-functional teams
Data product teams in a data mesh are typically cross-functional. They can include data engineers, data analysts, data scientists, and data product owners, allowing for full lifecycle data ownership within the team.
Additional factors to keep in kind for data vault
One of the biggest criticisms of data vault is its complexity. The model consists of many different types of tables (Hubs, Links, Satellites), and the relationships between them can become quite complex, especially in large systems.
Data vault requires a specific modeling technique that can be quite different from traditional data modeling methods. It requires a good understanding of the business and its entities and their relationships.
Given the complexity of the data vault model, automation is often recommended for creating and maintaining the data vault. This typically involves using specific data vault modeling and ETL tools.
4. Historical tracking
One of the key strengths of data vault is its ability to keep historical data, even when the underlying source systems change. This is due to the separation of business keys (Hubs), relationships (Links), and descriptive data (Satellites).
Because of its modular design, data vault can easily adapt to changes in business requirements or underlying systems. New source systems can be added, or existing ones can be modified, without significant impact on the existing data vault.
Understanding these aspects will help you determine how best to apply data mesh and data vault in your organization, and what kind of challenges you might face in their implementation.
Comparing data mesh and data vault: Unveiling the contrasts in a comparative table
Now, let us look at a comparative table for a high-level overview of the key differences and similarities between data mesh and data vault. Remember that each approach has its strengths and weaknesses, and the best choice depends on your business’s specific needs, existing architecture, and future plans.
|Data Mesh||Data Vault|
|Definition||An architectural paradigm that treats data as a product, decentralizing data domains and delegating them to cross-functional teams.||A specific type of data modeling methodology for designing agile, scalable data warehouses.|
|Use Cases||Works best for large-scale, domain-diverse organizations that aim to democratize data ownership and processing.||Ideal for companies dealing with complex, evolving data landscapes who need a flexible, adaptable data warehousing solution.|
|Ownership||Data is owned by cross-functional, domain-specific teams.||Data is owned by a centralized team, although it's flexible enough to accommodate decentralized access and management.|
|Handling Changes||Agile and adaptable, as each data domain is handled independently, yet under company-wide standards.||Agile and adaptable due to its modular design, allows for easy integration of new systems or changes.|
|Historical Tracking||Depends on the implementation by each data domain team.||Built-in historical tracking due to the separation of Hubs, Links, and Satellites.|
|Complexity||Organizational and cultural complexity due to shift towards decentralization. Technical complexity can vary based on each team's implementation.||Higher technical complexity due to the specific data modeling technique and relationships between different entities.|
|Infrastructure||Decentralized data domains can choose their own infrastructure, as long as they adhere to company-wide standards and interfaces.||Typically implemented in a centralized data warehouse, but the design is flexible and can be implemented on different infrastructures.|
|Best For||Businesses with a diverse set of data products need a high degree of autonomy and speed.||Businesses undergoing rapid changes, or those needing to integrate a variety of systems and data sources.|
Bringing it all together
Data mesh is a decentralized data architecture paradigm that treats data as a product. This means data is owned, maintained, and used by cross-functional, domain-specific teams, known as data product teams. It’s best for organizations with large, diverse sets of data where a monolithic, centralized architecture would be unwieldy or inefficient.
Data vault, on the other hand, is a specific type of data modeling methodology for creating scalable, flexible data warehouses. It’s best suited to complex, evolving data environments, where agility and adaptability are key.
In comparison, while both approaches aim to handle complex, large-scale data, they serve different purposes. Data mesh is about the organization of teams and ownership of data, whereas data vault is about the technical design of the data warehouse. The best approach depends on your organization’s specific context, needs, and goals.
Data mesh vs data vault: Related reads
- What is Data Mesh? - Examples, Case Studies, and Use Cases
- Data Mesh Principles — 4 Core Pillars & Logical Architecture
- Data Mesh Architecture: Core Principles, Components, and Why You Need It?
- Data Mesh Setup and Implementation - An Ultimate Guide
- How to Implement Data Mesh from a Data Governance Perspective?
- Snowflake Data Mesh: Step-by-Step Setup Guide
- Data Mesh Vs. Data Lake — Differences & Use Cases For 2023
- Data Mesh vs. Data Fabric: How do you choose the best approach for your business needs?
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