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A software company is building a new microservices-based application. They are using Azure Cosmos DB as their primary data store. They anticipate extremely high read loads (millions of reads per second) for certain frequently accessed data, but updates to this data are infrequent. To offload Cosmos DB and ensure consistent low-latency reads, they decide to implement an in-memory cache. Which Azure caching solution is best suited for this scenario, considering its high throughput, low latency, and integration capabilities?
- AAzure Cache for Redis
- BAzure SQL Database In-Memory OLTP
- CAzure Storage Account (Premium Block Blobs)
- DAzure Data Lake Storage Gen2
Show answer & explanationAnswer & explanation
Correct answer: A. Azure Cache for Redis
Azure Cache for Redis is a high-performance, in-memory data store that provides extremely low latency and high throughput for read-heavy workloads. Its popularity and robust feature set make it ideal for caching frequently accessed data in microservices architectures.
Why the other options are wrong
- B. Azure SQL Database In-Memory OLTP is for improving transaction performance within a SQL database, not a separate, distributed caching layer for other data stores like Cosmos DB.
- C. Azure Storage Account (Premium Block Blobs) is for persistent storage of large blobs, not for high-speed in-memory caching of frequently accessed small data items.
- D. Azure Data Lake Storage Gen2 is optimized for big data analytics workloads, not real-time, low-latency caching for operational applications.
Azure Cache for Redis
A fully managed, in-memory data store based on the open-source Redis. It provides high-performance and low-latency data access for applications.
- Supports various data structures (strings, hashes, lists).
- Ideal for caching, session management, and message brokering.
- Offers different tiers, including Enterprise for high availability and performance.
Memory trick: Redis for speed, when Cosmos needs a boost!