You are an Azure Solutions Architect designing a data modernization strategy for a global retail enterprise. The architecture must achieve an SLA of availability for both reads and writes, and support global distribution across three regions: East US, West Europe, and Southeast Asia. The application suite consists of multiple workloads with different read-to-write ratios and data models:
- Workload 1: A recommendation engine that performs relationship-heavy queries (vertices and edges) to map user preferences, requiring a read-to-write ratio.
- Workload 2: A globally distributed telemetry processor migrated from an on-premises Apache Cassandra cluster, requiring a read-to-write ratio.
- Workload 3: A product catalog migrated from an existing MongoDB cluster that uses complex document aggregation pipelines, requiring an read-to-write ratio.
- Workload 4: A simple session-state store migrated from Azure Table Storage requiring high-throughput key-value lookups with a read-to-write ratio.
Match each workload description to the most appropriate Azure Cosmos DB API to meet these requirements.
- A relationship-heavy recommendation engine that maps complex user connections using vertices and edges.Azure Cosmos DB for Apache Gremlin
- A high-write telemetry processor migrated from an Apache Cassandra cluster utilizing CQL.Azure Cosmos DB for Apache Cassandra
- A product catalog migrated from a MongoDB database using document aggregation pipelines.Azure Cosmos DB for MongoDB
- A session-state key-value store migrated from Azure Table Storage.Azure Cosmos DB for Table