Federation

Data Federation

Enhanced Definition

In the z/OS and enterprise computing context, **data federation** refers to the technology and process of integrating data from multiple, disparate data sources (which may include various mainframe databases like DB2, IMS, VSAM, or even external distributed databases) and presenting it to applications or users as a single, unified, virtual data source. This allows applications to query and manipulate data without needing to know the underlying physical location or structure of each individual source.

Key Characteristics

- Virtualization Layer: Creates a logical view of data, abstracting the complexities of underlying physical data sources and their differing data models.
- Heterogeneous Data Source Support: Can integrate data from diverse sources, including relational databases (DB2), hierarchical databases (IMS), indexed files (VSAM), and often distributed RDBMS (Oracle, SQL Server) or cloud data stores.
- Real-time Access: Typically provides real-time or near real-time access to the federated data, rather than requiring batch data replication or ETL processes.
- Query Optimization: Includes mechanisms to optimize queries across multiple sources, pushing down predicates and joins to the source systems where possible to minimize data transfer.
- Metadata Management: Requires robust metadata management to define the federated view, map it to source data, and handle data type conversions and transformations.
- Security and Governance: Inherits or applies security policies across federated sources, often integrating with z/OS security managers like RACF or ACF2.

Use Cases

- Enterprise Data Integration: Providing a unified view of customer, product, or financial data that resides across various mainframe and distributed systems for reporting, analytics, or new application development.
- Legacy Modernization: Allowing newer applications (e.g., Java, .NET) to access mainframe data (DB2, IMS, VSAM) alongside distributed data through a standard interface (e.g., SQL) without direct knowledge of mainframe access methods.
- Business Intelligence & Analytics: Enabling BI tools to query and analyze data from multiple operational systems, both on and off the mainframe, as if it were in a single data warehouse.
- Data Migration & Consolidation: Facilitating phased data migration or consolidation projects by providing a consistent access layer during the transition period.
- Regulatory Compliance: Simplifying data access for auditing and compliance reporting by presenting a consolidated view of required data across disparate systems.

Related Concepts

Data federation complements and sometimes contrasts with data warehousing and ETL (Extract, Transform, Load). While ETL moves and transforms data into a centralized warehouse, federation provides a virtual, real-time view without physical data movement. It often leverages middleware technologies and database connectors to interact with various data sources. It's also closely related to data virtualization and enterprise information integration (EII), aiming to simplify data access for applications and users.

Best Practices:

Understand Data Requirements: Clearly define the data elements, relationships, and performance expectations for the federated views before implementation.
Optimize Source Systems: Ensure that underlying source systems are well-tuned and indexed to support efficient query execution from the federation layer.
Minimize Data Movement: Design federated queries to push down processing to source systems as much as possible, reducing the amount of data transferred across the network.
Robust Metadata Management: Maintain accurate and up-to-date metadata for all federated sources and views to ensure data integrity and query correctness.