A DataHub architecture is a modern approach to managing and using data within an organization. It provides a centralized platform for integrating, managing, and leveraging data from disparate sources. The key principles of a DataHub architecture include:
Centralized Data Platform
A DataHub provides a single platform where all of an organization’s structured and unstructured data is consolidated. This creates a “single source of truth” and eliminates data silos. The DataHub ingests data from source systems via batch ETL jobs, streaming data pipelines, or direct queries. It then standardizes, enriches, and stores this data for downstream consumption.
Schema Management
A DataHub maintains a catalog of schemas and metadata to describe all the datasets within the Hub. This includes technical metadata like table definitions and columns, as well as business metadata like data definitions, tags, ownership info, etc. The schema catalog provides a simple way for users to understand what data is available and how it can be used.
Self-Service Data Access
Users are able to directly query and analyze data within the DataHub through SQL, or via orchestration engines that can serve data to downstream applications. This self-service access eliminates dependencies on technical teams for basic data needs. Queries, jobs, models, apps, etc. can all leverage the DataHub as a central data access layer.
Governance and Security
DataHub provides data governance capabilities like data discovery, lineage tracking, policy management, and role-based access controls. This helps organizations govern data as an asset and comply with regulations. All activity within the DataHub is audited through detailed monitoring, logging, and metadata change tracking.
Agility and Extensibility
A DataHub is designed to easily adapt as data needs change. The platform provides automated scalability and flexibility to ingest new data sources. The modular architecture also allows extensibility through custom extensions, plugins, and integration with external apps.
Open Technologies
DataHubs leverage open source technologies and avoid vendor lock-in. This includes open source data platforms like Apache Hadoop, Spark, Hive, HBase, etc. Cloud-native technologies like containers, microservices, Kubernetes, and serverless functions are also commonly used.
Unified Analytics
The DataHub serves as a foundation for unified analytics, supporting both batch and real-time use cases. Batch pipelines can leverage data in the Hub for ETL, reporting, and machine learning. Real-time applications can run queries for online analytics, personalization, and predictive capabilities.
Implementing a DataHub
Implementing a DataHub architecture involves several key steps:
Planning and Scoping
– Define business goals and intended usages of the DataHub
– Inventory existing data sources and infrastructure
– Prioritize initial datasets and use cases to tackle
– Determine required integrations, access patterns, and governance needs
Design and Build
– Select DataHub technologies and cloud platform
– Design architecture, schemas, ETL/ELT jobs, security model etc.
– Develop core DataHub platform and ingest initial datasets
– Implement access and governance mechanisms
Iteration and Extension
– Onboard new users and use cases, provide self-service access
– Iterate platform based on user feedback to extend capabilities
– Ingest new data sources and build pipelines for new use cases
– Scale DataHub to meet growth in data volume and usage
Ongoing Management
– Monitor data quality, system health, and SLAs
– Manage costs by optimizing infrastructure and cloud spend
– Maintain security, access controls, and compliance over time
– Regularly back up data and update DRP procedures
DataHub Architecture Components
A DataHub implementation typically includes the following key components:
| Component | Description |
|---|---|
| Metadata Store | Central repository to store structural and operational metadata for all data within the DataHub |
| Processing Engine | Performs data transformation and loading processes to ingest and prepare data from sources |
| Orchestration Layer | Tools and services to define and manage ETL/ELT workflows that populate the DataHub |
| Storage Layer | Distributed file storage and database technologies to store and query data at scale |
| Access Services | Services and interfaces that enable users to discover and consume data for downstream use cases |
| Governance Services | Tools and policies to manage user access, enforce data standards, and ensure compliance |
| Monitoring & Admin | Tools to monitor health of DataHub components and provide admin/ops functionality |
Metadata Management
Metadata management is a critical part of an effective DataHub implementation. The metadata store tracks key attributes about every dataset including:
- Technical metadata – Physical storage details, data types/schemas, pipelines or jobs that populate the dataset
- Business metadata – Data definitions, ownership info, tags, domain info, sensitivity, quality measures
- Operational metadata – Stats on usage, SLAs, data lineage, reference data mapping
- Governance metadata – Access permissions, retention policies, regulatory compliance data
This metadata helps users find relevant data for their needs and enables governance of data as an enterprise asset. Metadata collection should be automated as much as possible. Tools like Apache Atlas, Alation, and Collibra are often used for metadata management within a DataHub.
Self-Service Data Access
A key goal of DataHub architecture is enabling self-service access to data through standard interfaces. This empowers users to tap into data without overhead. Common self-service capabilities include:
- Data discovery – Browsing data catalog and metadata to find relevant datasets
- Direct access – Querying data via interfaces like SQL, Spark, and Pandas
- Search – Searching metadata and data content to find relevant info
- BI/Visualization – Connecting BI tools like Tableau directly to DataHub to analyze data
- Orchestration – Executing pre-built scripts/workflows to prepare data for use cases
- Notebooks – Developing interactive notebooks (Jupyter, Zeppelin etc.) to explore and model data
Effective self-service reduces time spent on data preparation and enables more users to leverage data and analytics within their domain.
Data Governance Capabilities
A DataHub provides tooling and policies to govern data as an enterprise asset and enable compliance. Key governance capabilities include:
Data Discovery
– Search and explore available datasets
– Understand what data exists via metadata
– Identify data owners, definitions, lineage etc.
Data Lineage
– Track data from sources through transformations
– Identify dependencies and upstream data sets
– Audit changes to data over time
Data Quality
– Assess data quality with metrics, rules, profiling
– Monitor quality trends over time
– Trigger alerts for quality issues
Data Security
– Authentication and access controls
– Encryption both at rest and in motion
– Role based permissions to data
Regulatory Compliance
– Classify sensitive data
– Manage data retention policies
– Right to be forgotten and data deletion
– Reporting and auditing capabilities
DataHub Implementation Challenges
While a DataHub can provide tremendous value, implementing one involves notable challenges including:
Integrating Disparate Data Sources
Ingesting and standardizing data from many different source systems with varying formats, schemas, and semantics.
achieving Scalability
Handling large volumes of data ingestion and query workloads, and scaling both storage and processing.
Managing Schema Evolution
Safely modifying schemas over time as data requirements evolve.
Providing Unified Access
Exposing data via consistent interfaces for both batch and real-time use cases.
Governing Usage
Monitoring and controlling how data is used to enforce security, compliance, and data quality.
Minimizing Disruption
Implementing DataHub capabilities without disrupting existing data and analytics flows.
Driving Adoption
Getting users to leverage the DataHub instead of accessing data directly.
Controlling Costs
Carefully managing infrastructure, services, and operational overhead.
DataHub vs. Data Lake
DataHubs share similarities with data lakes but have some key differences:
| DataHub | Data Lake |
|---|---|
| Includes business metadata for discoverability | Stores just raw data |
| Governed usage with security and access controls | Minimal governance, open access |
| Prepared datasets usable without transformation | Requires processing before analysis |
| Designed for structured consumption | Ad hoc analytics and exploration |
| Integration and orchestration layer | Just raw storage, no orchestration |
In some cases, a DataHub may be deployed alongside a data lake to leverage it for storage while providing governance and semantics on top.
DataHub Benefits
The benefits of implementing a DataHub architecture include:
- Single source of truth – One place for all enterprise data
- Improved discoverability – Metadata simplifies data discovery
- Self-service access -Users can tap data directly without dependencies
- Data agility – Faster, easier to adapt as needs change
- Unified analytics – Enables both batch and real-time use cases
- Data democratization – More users can access and analyze data
- Improved data quality – Consistent QA helps improve trust in data
- Enhanced governance – Discoverability, lineage, access controls
- Greater efficiency – Less redundant ETL and access mechanisms
By consolidating data in a governed, well-managed hub organizations can maximize the business value derived from their data assets.
DataHub Use Cases
DataHubs support a wide array of data-driven use cases including:
Analytics and Reporting
– BI reporting and dashboards
– Ad hoc analytics and data exploration
– Data science experiments and modeling
Data Pipelines and ETL
– Batch data integration from enterprise systems
– Internet of things and real-time data ingestion
– Orchestration of data movement and transformations
Data Applications
– Powering internal and external apps with unified data services
– Low latency queries for real-time apps and personalization
– Machine learning model deployment and scoring
Regulatory Compliance
– Data retention and privacy policies
– Securing and controlling sensitive data
– Audit logging and lineage tracking
Conclusion
A DataHub architecture provides a scalable, governed data platform that breaks down data silos and enables more ways to derive value from data. By providing a single source of truth with management of security, access, and metadata, DataHubs make data easier to discover, trust, and use across the organization. This model will continue gaining adoption as data volumes grow and organizations aim to democratize analytics, power new applications, and govern data as an enterprise asset.
