Cassandra vs. InfluxDB vs. Hazelcast

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Heads up: you're comparing apples to oranges. Tools belonging to different groups are often not directly comparable because they usually have different intended purposes and benefits.

Cassandra

InfluxDB

Hazelcast

Favorites

57

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32

Favorites

8

Hacker News, Reddit, Stack Overflow Stats

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GitHub Stats

Description

What is Cassandra?

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

What is InfluxDB?

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

What is 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.

Pros about this tool

Pros
Why do you like Cassandra?

Pros
Why do you like InfluxDB?

Pros
Why do you like Hazelcast?

Cons about this tool

Cons
Cons
No Cons submitted yet for InfluxDB
Cons
No Cons submitted yet for Hazelcast

Pricing

Companies

277 Companies Using Cassandra
89 Companies Using InfluxDB
19 Companies Using Hazelcast

Integrations

Cassandra Integrations
InfluxDB Integrations
Hazelcast Integrations

Latest News

Cassandra Load Testing with Groovy
Using Instaclustr and Cassandra with Kong
Cassandra Query Language
A Deep Dive into InfluxDB
Spring Integration Extension for Hazelcast 1.0.0 GA ...
Build a Spring Boot API with Hazelcast for Cached Us...


Interest Over Time


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