CouchDB vs BigMemory

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

CouchDB is a Development solution with tags like nosql, json, documentoriented, scalable.

It boasts features such as Document oriented database, JSON document storage, JavaScript as query language, MapReduce for aggregation, Master-master replication, RESTful API and pros including High availability, Easy horizontal scalability, Schema-less, JSON data model, Powerful query capabilities.

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.

Feature	CouchDB	BigMemory
Overall Score	1	N/A
Primary Category	Development	Development

Product Overview

CouchDB

Description: CouchDB is an open-source NoSQL document-oriented database that focuses on ease of use and scalability. It uses JSON documents and JavaScript as its query language, allowing you to store, access, and manage data in a simple yet flexible way.

BigMemory

Description: 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.

Key Features Comparison

CouchDB Features

• Document oriented database
• JSON document storage
• JavaScript as query language
• MapReduce for aggregation
• Master-master replication
• RESTful API

BigMemory Features

• 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

Pros & Cons Analysis

CouchDB

Pros

• High availability
• Easy horizontal scalability
• Schema-less
• JSON data model
• Powerful query capabilities

Cons

• Not suitable for complex transactions
• Limited query flexibility compared to SQL
• Steep learning curve

BigMemory

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

Pricing Comparison

CouchDB

• Open Source

BigMemory

• Subscription-Based