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A Data Hub is innovative architecture that streamline data integration, serves as a vital intermediary between data producers and data consumers, providing a centralised point for the exchange of information. This architectural approach is designed to offer businesses a reliable and accurate single source of truth for their data.
The Data Hub encompasses a collection of technologies and tools that empower data producers to efficiently transmit their data to a data platform in either batch mode (cold path) or stream mode (hot path). Once the data reaches the platform, it is securely stored, and a series of data quality processes are applied to validate and enhance its overall quality. Following the data quality processes, the data is harmonised, enabling the application of AI/ML models to enrich the dataset by generating predictive insights. To ensure responsible and effective data utilisation, data governance measures (Data quality, Data compliances, Data access rights, data usability) are implemented, providing the appropriate access and usage guidelines for the data. Typically, data governance team is the one who is responsable and accountable to define these guidelines, processes and procedure.
The Data Hub architecture enables data sharing with data consumers in both batch mode and stream mode.
The implementation of a Data Hub architecture offers a significant advantage by establishing a centralised location for data, enabling seamless connections between various data touch points. This approach not only enhances data security but also provides companies with a cost-effective data integration system and agile tools for building and managing diverse data integration processes.
One of the key differentiators between a Data Hub and a data warehouse is their respective purposes. Although both architectures are designed for structured and semi-structured data, a data warehouse is primarily intended for managing analytical data. In contrast, a Data Hub has limited analytics capabilities as its storage by default is not optimised for analytics. As a result, it’s slow when dealing with complex queries and unsuitable for processing historical data. However, the data hub can be used in conjunction with data warehouse and data lake to overcome this challenge.
Data Hub and Data Mesh are two architectural approaches to managing data in organisations. While they have similarities, they differ in their overall approach and emphasis.
Data Mesh is a decentralised data architecture (as opposite to Data Hub which is centralised data architecture) that aims to distribute data ownership and responsibility across different teams or domains within an organisation. Instead of centralising data in a single repository, Data Mesh advocates for a federated approach where each domain or team is responsible for their own data products and services. It focuses on empowering domain experts to manage their data and treat it as a product. Data Mesh promotes self-serve data infrastructure, data as a product mindset, and domain-oriented decentralised teams.
One of the key principles of Data Mesh is the creation of a domain-driven distributed architecture. This principle establishes Data Mesh as a decentralised data architecture, setting it apart as a significant differentiator.
Now, the question arises: Is it possible to create a hybrid architecture that incorporates the best features of both Data Mesh and Data Hub? It is a debatable question. In theory, this hybrid architecture can be achieved by segregating data domains as data hubs while maintaining centralised governance and control over certain aspects.
One of the main challenges with this approach is to avoid data duplication across the domains as it will disrupt the concept of a single source of truth. Additionally, another challenge is the increased complexity of the solution.
In practice, implementing a Data Hub as an enterprise data integration solution may encounter several challenges:
1. Application Integration: It is important to note that a Data Hub does not replace iPaaS or ESB for certain use cases (e.g. real-time application integration). However, Data hub can use iPaas features to provide certain capabilities (e.g. publish and subscription).
2. Data Governance: As data from diverse sources is combined in a Data Hub, concerns about data ownership may arise. However, this point can be addressed through a hybrid approach with Data Mesh, as discussed in the previous section.
3. Data Observability: With a large amount of data and complex data processing, monitoring data quality becomes challenging. To tackle this, it is recommended to implement advanced AI-based tools that can detect anomalies and ensure data observability.
4. Maintenance and Support: Maintaining and supporting a Data Hub can be a significant task as it requires ongoing effort to ensure system availability.
While a Data Hub as an enterprise data integration solution offers significant potential, the reality of implementation involves addressing these challenges and finding practical solutions. Organisations must consider factors such as data complexity, organisational dynamics, technical capabilities, scalability, and governance to ensure successful implementation and realisation of the intended benefits.
- Dehghani, Z. Data Mesh: Delivering Data-Driven Value at Scale (1st ed.). O’Reilly.
- Data Mesh thesis
- Data Mesh Learning Community
- Building a Data Mesh: A beginners guide
- What is a Data Mesh and How Not to Mesh it Up
- More Resources like this here
