Apache Ignite vs Azure Redis Cache

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Apache Ignite

95
165
+ 1
32
Azure Redis Cache

58
123
+ 1
7
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Apache Ignite vs Azure Redis Cache: What are the differences?

Introduction

In this article, we will discuss the key differences between Apache Ignite and Azure Redis Cache.

  1. Language Support: Apache Ignite supports multiple programming languages such as Java, .NET, C++, and Node.js, making it compatible with a wide range of applications. On the other hand, Azure Redis Cache primarily focuses on providing support for programming languages commonly used in the Microsoft ecosystem, such as C#, Java, Python, and JavaScript.

  2. Cache Architecture: Apache Ignite offers a distributed in-memory cache architecture that allows for storing a large amount of data in memory, providing excellent performance for data-intensive applications. In contrast, Azure Redis Cache follows a traditional cache architecture where data is stored in memory, but it also supports persistence to disk.

  3. Distributed Computations: One significant difference between Apache Ignite and Azure Redis Cache is their support for distributed computations. Apache Ignite provides a built-in compute grid that enables parallel execution of complex computations across a cluster of nodes. On the other hand, Azure Redis Cache does not offer a similar built-in feature for distributed computations.

  4. Data Partitioning: Apache Ignite offers flexible data partitioning strategies, allowing for efficient data distribution across a cluster of nodes, reducing the chances of hotspots and ensuring high scalability. In contrast, Azure Redis Cache does not provide granular control over data partitioning and relies on a sharding mechanism that is managed by Microsoft.

  5. Data Durability and Persistence: Apache Ignite provides options for data durability and persistence, with features such as write-through, write-behind, and disk-based persistence. This ensures that data is not lost in the event of node failures or system restarts. In contrast, Azure Redis Cache primarily focuses on in-memory caching and does not offer built-in mechanisms for data persistence.

  6. Pricing Model: Apache Ignite follows an open-source model, allowing users to use it for free without any licensing costs. However, there may be costs associated with infrastructure, support, and maintenance. Azure Redis Cache, being a managed service provided by Microsoft, has its pricing model based on factors such as cache size, data transfer, and throughput.

In summary, Apache Ignite offers broader language support, a distributed compute grid, flexible data partitioning, and data durability features, while Azure Redis Cache primarily focuses on in-memory caching with limited language support and persistence options. Additionally, Apache Ignite follows an open-source model, while Azure Redis Cache has a pricing model based on usage.

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Pros of Apache Ignite
Pros of Azure Redis Cache
  • 4
    Multiple client language support
  • 4
    Written in java. runs on jvm
  • 4
    Free
  • 4
    High Avaliability
  • 3
    Load balancing
  • 3
    Sql query support in cluster wide
  • 3
    Rest interface
  • 2
    Easy to use
  • 2
    Distributed compute
  • 2
    Better Documentation
  • 1
    Distributed Locking
  • 4
    Cache-cluster
  • 3
    Redis

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What is Apache Ignite?

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

What is Azure Redis Cache?

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

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What tools integrate with Apache Ignite?
What tools integrate with Azure Redis Cache?
What are some alternatives to Apache Ignite and Azure Redis Cache?
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
Hazelcast
With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
See all alternatives