Azure Cosmos DB vs BigMemory

Struggling to choose between Azure Cosmos DB and BigMemory? Both products offer unique advantages, making it a tough decision.

Azure Cosmos DB is a Ai Tools & Services solution with tags like nosql, document-database, microsoft-azure, cloud-database.

It boasts features such as Globally distributed database, Multiple data models (document, key-value, wide-column, graph), Automatic indexing and querying, Multi-master replication, Tunable consistency levels, Serverless or provisioned throughput, SLAs for high availability, Encryption at rest and in transit and pros including High scalability and availability, Low latency worldwide access, Multiple APIs and SDKs, Automatic indexing and querying, Flexible data models, Serverless option reduces ops overhead.

On the other hand, BigMemory is a Development product tagged with caching, data-management, low-latency.

Its standout features include Distributed in-memory data storage, Automatic data eviction and loading, Read/write caching for databases, Support for terabytes of data, Integration with Hadoop and Spark, High availability through replication and failover, and it shines with pros like Very fast data access and throughput, Reduces load on databases, Scales horizontally, Lowers infrastructure costs by using RAM instead of disks, Supports both Java and .NET platforms.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Azure Cosmos DB

Azure Cosmos DB

Azure Cosmos DB is a globally distributed, multi-model database service by Microsoft for mission-critical applications. It supports document, key-value, wide-column, and graph databases, and provides APIs for multiple platforms.

Categories:
nosql document-database microsoft-azure cloud-database

Azure Cosmos DB Features

  1. Globally distributed database
  2. Multiple data models (document, key-value, wide-column, graph)
  3. Automatic indexing and querying
  4. Multi-master replication
  5. Tunable consistency levels
  6. Serverless or provisioned throughput
  7. SLAs for high availability
  8. Encryption at rest and in transit

Pricing

  • Pay-As-You-Go
  • Subscription-Based

Pros

High scalability and availability

Low latency worldwide access

Multiple APIs and SDKs

Automatic indexing and querying

Flexible data models

Serverless option reduces ops overhead

Cons

Can be more expensive than other databases

Steep learning curve for some features

Limited query support compared to SQL databases

Vendor lock-in


BigMemory

BigMemory

BigMemory is an in-memory data management system that provides a fast, scalable cache and data store for applications. It allows storing terabytes of data in memory for low-latency data access.

Categories:
caching data-management low-latency

BigMemory Features

  1. Distributed in-memory data storage
  2. Automatic data eviction and loading
  3. Read/write caching for databases
  4. Support for terabytes of data
  5. Integration with Hadoop and Spark
  6. High availability through replication and failover

Pricing

  • Subscription-Based

Pros

Very fast data access and throughput

Reduces load on databases

Scales horizontally

Lowers infrastructure costs by using RAM instead of disks

Supports both Java and .NET platforms

Cons

Can lose data if not persisted

RAM is more expensive than disk

Not fully ACID compliant

Can be complex to configure and tune