Redis vs BigMemory

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

Redis is a Development solution with tags like caching, inmemory, keyvalue-store.

It boasts features such as 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 pros including 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.

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.

Redis

Redis

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.

Categories:
Development Databases
caching inmemory keyvalue-store

Redis Features

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

Pricing

  • Open Source

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

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:
Development In-Memory Databases
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