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Hazelcast

243
365
+ 1
56
Kafka Manager

63
143
+ 1
1
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Hazelcast vs Kafka Manager: What are the differences?

Developers describe Hazelcast as "Clustering and highly scalable data distribution platform for Java". 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. On the other hand, Kafka Manager is detailed as "A tool for managing Apache Kafka, developed by Yahoo". This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

Hazelcast belongs to "In-Memory Databases" category of the tech stack, while Kafka Manager can be primarily classified under "Message Queue".

Some of the features offered by Hazelcast are:

  • Distributed implementations of java.util.{Queue, Set, List, Map}
  • Distributed implementation of java.util.concurrent.locks.Lock
  • Distributed implementation of java.util.concurrent.ExecutorService

On the other hand, Kafka Manager provides the following key features:

  • Manage multiple clusters
  • Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution)
  • Run preferred replica election

Hazelcast and Kafka Manager are both open source tools. Kafka Manager with 7.45K GitHub stars and 1.82K forks on GitHub appears to be more popular than Hazelcast with 3.15K GitHub stars and 1.15K GitHub forks.

According to the StackShare community, Hazelcast has a broader approval, being mentioned in 25 company stacks & 15 developers stacks; compared to Kafka Manager, which is listed in 8 company stacks and 4 developer stacks.

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Pros of Hazelcast
Pros of Kafka Manager
  • 10
    High Availibility
  • 6
    Distributed Locking
  • 5
    Distributed compute
  • 5
    Sharding
  • 4
    Load balancing
  • 3
    Sql query support in cluster wide
  • 3
    Map-reduce functionality
  • 3
    Written in java. runs on jvm
  • 3
    Publish-subscribe
  • 2
    Performance
  • 2
    Simple-to-use
  • 2
    Multiple client language support
  • 2
    Rest interface
  • 2
    Optimis locking for map
  • 1
    Super Fast
  • 1
    Admin Interface (Management Center)
  • 1
    Better Documentation
  • 1
    Easy to use
  • 1
    Better Insights for Kafka cluster

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Cons of Hazelcast
Cons of Kafka Manager
  • 3
    License needed for SSL
    Be the first to leave a con

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    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.

    What is Kafka Manager?

    This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Hazelcast?
    What companies use Kafka Manager?
    See which teams inside your own company are using Hazelcast or Kafka Manager.
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    What tools integrate with Hazelcast?
    What tools integrate with Kafka Manager?

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    What are some alternatives to Hazelcast and Kafka Manager?
    Redis
    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    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.
    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.
    Memcached
    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
    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
    See all alternatives