BigMemory vs Redis

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

BigMemory is a Development solution with tags like caching, data-management, low-latency.

It boasts features such as 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 pros including 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.

On the other hand, Redis is a Development product tagged with caching, inmemory, keyvalue-store.

Its standout features include In-memory data structure store, Supports various data structures (strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, geospatial indexes, streams), Used as a database, cache, and message broker, Provides high performance and low latency, Supports replication, clustering, and high availability, Supports a wide range of programming languages, Provides a rich set of commands and APIs, Supports data persistence (RDB and AOF), and it shines with pros like High performance and low latency, Flexible and versatile data structures, Supports a wide range of use cases, Easy to set up and configure, Scalable and highly available, Open-source and free to use.

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

Product Overview

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.

Redis

Description: Redis is an open-source, in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes and streams.

Key Features Comparison

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

Redis Features

• In-memory data structure store
• Supports various data structures (strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, geospatial indexes, streams)
• Used as a database, cache, and message broker
• Provides high performance and low latency
• Supports replication, clustering, and high availability
• Supports a wide range of programming languages
• Provides a rich set of commands and APIs
• Supports data persistence (RDB and AOF)

Pros & Cons Analysis

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

Redis

Pros

• High performance and low latency
• Flexible and versatile data structures
• Supports a wide range of use cases
• Easy to set up and configure
• Scalable and highly available
• Open-source and free to use

Cons

• In-memory nature can lead to data loss in case of system failures
• Complexity in setting up and maintaining a highly available Redis cluster
• Limited support for transactions and complex queries compared to traditional databases
• Potential for high memory usage, especially for large datasets

Pricing Comparison

BigMemory

• Subscription-Based

Redis

• Open Source