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Apache Ignite

87
152
+ 1
31
VoltDB

16
62
+ 1
18
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Apache Ignite vs VoltDB: What are the differences?

Developers describe Apache Ignite as "An open-source distributed database, caching and processing platform *". It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale. On the other hand, *VoltDB** is detailed as "In-memory relational DBMS capable of supporting millions of database operations per second". VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Apache Ignite and VoltDB can be categorized as "In-Memory Databases" tools.

Some of the features offered by Apache Ignite are:

  • Memory-Centric Storage
  • Distributed SQL
  • Distributed Key-Value

On the other hand, VoltDB provides the following key features:

  • In-Memory Performance with On-Disk Durability
  • Transparent Scalability with Data Consistency
  • NewSQL – All the benefits of SQL with Unlimited Scalability

Apache Ignite is an open source tool with 2.67K GitHub stars and 1.3K GitHub forks. Here's a link to Apache Ignite's open source repository on GitHub.

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Pros of Apache Ignite
Pros of VoltDB
  • 4
    Free
  • 4
    Written in java. runs on jvm
  • 3
    Load balancing
  • 3
    High Avaliability
  • 3
    Multiple client language support
  • 3
    Sql query support in cluster wide
  • 3
    Rest interface
  • 2
    Easy to use
  • 2
    Better Documentation
  • 1
    Distributed Locking
  • 1
    gtj
  • 1
    Distributed compute
  • 1
    Benfica
  • 5
    SQL + Java
  • 4
    In-memory database
  • 4
    A brainchild of Michael Stonebraker
  • 3
    Very Fast
  • 2
    NewSQL

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- No public GitHub repository available -

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 VoltDB?

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

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What companies use Apache Ignite?
What companies use VoltDB?
See which teams inside your own company are using Apache Ignite or VoltDB.
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What tools integrate with Apache Ignite?
What tools integrate with VoltDB?
What are some alternatives to Apache Ignite and VoltDB?
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