This section explores common architectural patterns used in big data systems. Understanding these patterns is crucial for designing scalable and reliable solutions.
Here are some key patterns:
This section provides a brief overview of the patterns used. The specific choice depends on the application and its requirements.
We'll focus on the core principles and common implementations.
Pattern: Layered Data Storage
This approach utilizes distinct layers for data access and management, improving scalability and maintainability. Different layers handle data formats and processing needs.
Link: /msdn/blog/data-storage-layer-architecture
Pattern: Data Virtualization
Enables accessing and manipulating data regardless of its location – enabling a unified data view.
Link: /msdn/blog/data-virtualization-concepts
Pattern: Distributed Processing Frameworks
These frameworks are designed for parallel processing of large datasets across multiple nodes, significantly improving processing speed.
Link: /msdn/blog/distributed-processing-frameworks
Pattern: Message Queues & Event Streaming
Used to ingest, process, and distribute real-time data streams, ensuring data availability and reliability.
Link: /msdn/blog/message-queues-event-streaming