An Operational Data Layer (or ODL) is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. It enables a range of board-level strategic initiatives such as Legacy Modernization and Data as a Service, and use cases such as single view, real-time analytics, and mainframe offload.
An Operational Data Layer is an intermediary between existing data sources and consumers that need to access that data. An ODL deployed in front of legacy systems can enable new business initiatives and meet new requirements that the existing architecture can’t handle – without the difficulty and risk of a full rip and replace of legacy systems.
In this white paper, we examine the Operational Data Layer architecture pattern, covering:
- Why to implement an ODL
- Common use cases and application categories
- Source systems and data producers
- Consuming systems and data access
- Data loading
- Data flow and an ODL maturity model
- Technical requirements
- Operational Data Layers in action
Download this white paper to learn more about Operational Data Layers and whether it makes sense to implement one in your organization.
Companies ranging from startups to Fortune 500s choose MongoDB to build, scale, and innovate.