Hazelcast vs Kafka Manager

Overview

Hazelcast

Stacks427

Followers474

Votes59

GitHub Stars6.4K

Forks1.9K

Kafka Manager

Stacks70

Followers173

Votes1

Hazelcast vs Kafka Manager: What are the differences?

Scalability and Data Storage: Hazelcast is an in-memory data grid platform that provides distributed, scalable processing and storage of data across a cluster of servers, allowing for high availability and fault tolerance. On the other hand, Kafka Manager is a tool specifically designed for managing Apache Kafka clusters, focusing on distributed streaming and processing of data streams rather than in-memory data storage like Hazelcast.
Data Processing Model: Hazelcast supports a distributed computing model where data is processed in parallel across multiple nodes in the cluster, enabling real-time analytics and computation on large datasets. In contrast, Kafka Manager is designed for real-time data streaming and processing, facilitating the movement of data from producers to consumers in a fault-tolerant, high-throughput manner.
Use Cases: Hazelcast is commonly used for caching, real-time processing, and maintaining consistency across distributed systems, making it suitable for applications requiring fast access to shared data and computation. Kafka Manager, on the other hand, is ideal for use cases involving real-time data processing, event streaming, and building data pipelines, enabling organizations to capture, process, and analyze streaming data at scale.
Event Processing: While Hazelcast supports event listeners and distributed event processing mechanisms for real-time data synchronization and processing, Kafka Manager focuses on managing topics, partitions, and consumer groups within Kafka clusters for efficient event streaming and processing.
Ease of Use: Hazelcast provides a user-friendly interface and APIs for developers to interact with the distributed cache and processing capabilities, making it relatively easy to integrate into existing applications. Kafka Manager offers a graphical user interface for monitoring and managing Kafka clusters, simplifying the deployment and maintenance of Kafka infrastructure for streaming applications.
Integration with Ecosystem: Hazelcast integrates well with various programming languages, frameworks, and technologies, offering versatile support for application development and deployment. In contrast, Kafka Manager is tightly integrated with Apache Kafka and related tools for seamless management of Kafka clusters and stream processing workflows.

In Summary, Hazelcast and Kafka Manager differ in their scalability and data storage approach, data processing model, use cases, event processing mechanisms, ease of use, and integration within the broader ecosystem.

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CLI (Node.js)

Manual

Detailed Comparison

Hazelcast	Kafka Manager
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.	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.
Distributed implementations of java.util.{Queue, Set, List, Map};Distributed implementation of java.util.concurrent.locks.Lock;Distributed implementation of java.util.concurrent.ExecutorService;Distributed MultiMap for one-to-many relationships;Distributed Topic for publish/subscribe messaging;Synchronous (write-through) and asynchronous (write-behind) persistence;Transaction support;Socket level encryption support for secure clusters;Second level cache provider for Hibernate;Monitoring and management of the cluster via JMX;Dynamic HTTP session clustering;Support for cluster info and membership events;Dynamic discovery, scaling, partitioning with backups and fail-over	Manage multiple clusters;Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution);Run preferred replica election;Generate partition assignments (based on current state of cluster);Run reassignment of partition (based on generated assignments)
Statistics
GitHub Stars 6.4K	GitHub Stars -
GitHub Forks 1.9K	GitHub Forks -
Stacks 427	Stacks 70
Followers 474	Followers 173
Votes 59	Votes 1
Pros & Cons
Pros 11 High Availibility 6 Distributed compute 6 Distributed Locking 5 Sharding 4 Load balancing Cons 4 License needed for SSL	Pros 1 Better Insights for Kafka cluster
Integrations
Java Spring	Kafka

What are some alternatives to Hazelcast, Kafka Manager?

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.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

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

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

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.

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

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.

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

Related Comparisons

Hazelcast vs Kafka Manager: What are the differences?

Scalability and Data Storage: Hazelcast is an in-memory data grid platform that provides distributed, scalable processing and storage of data across a cluster of servers, allowing for high availability and fault tolerance. On the other hand, Kafka Manager is a tool specifically designed for managing Apache Kafka clusters, focusing on distributed streaming and processing of data streams rather than in-memory data storage like Hazelcast.
Data Processing Model: Hazelcast supports a distributed computing model where data is processed in parallel across multiple nodes in the cluster, enabling real-time analytics and computation on large datasets. In contrast, Kafka Manager is designed for real-time data streaming and processing, facilitating the movement of data from producers to consumers in a fault-tolerant, high-throughput manner.
Use Cases: Hazelcast is commonly used for caching, real-time processing, and maintaining consistency across distributed systems, making it suitable for applications requiring fast access to shared data and computation. Kafka Manager, on the other hand, is ideal for use cases involving real-time data processing, event streaming, and building data pipelines, enabling organizations to capture, process, and analyze streaming data at scale.
Event Processing: While Hazelcast supports event listeners and distributed event processing mechanisms for real-time data synchronization and processing, Kafka Manager focuses on managing topics, partitions, and consumer groups within Kafka clusters for efficient event streaming and processing.
Ease of Use: Hazelcast provides a user-friendly interface and APIs for developers to interact with the distributed cache and processing capabilities, making it relatively easy to integrate into existing applications. Kafka Manager offers a graphical user interface for monitoring and managing Kafka clusters, simplifying the deployment and maintenance of Kafka infrastructure for streaming applications.
Integration with Ecosystem: Hazelcast integrates well with various programming languages, frameworks, and technologies, offering versatile support for application development and deployment. In contrast, Kafka Manager is tightly integrated with Apache Kafka and related tools for seamless management of Kafka clusters and stream processing workflows.

Hazelcast vs Kafka Manager

Overview