ActiveMQ vs Hadoop

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

ActiveMQ

Stacks879

Followers1.3K

Votes77

GitHub Stars2.4K

Forks1.5K

ActiveMQ vs Hadoop: What are the differences?

Developers describe ActiveMQ as "A message broker written in Java together with a full JMS client". Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License. On the other hand, Hadoop is detailed as "Open-source software for reliable, scalable, distributed computing". The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

ActiveMQ and Hadoop are primarily classified as "Message Queue" and "Databases" tools respectively.

"Open source" is the top reason why over 9 developers like ActiveMQ, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.

ActiveMQ and Hadoop are both open source tools. It seems that Hadoop with 9.27K GitHub stars and 5.78K forks on GitHub has more adoption than ActiveMQ with 1.51K GitHub stars and 1.05K GitHub forks.

According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to ActiveMQ, which is listed in 33 company stacks and 17 developer stacks.

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Advice on Hadoop, ActiveMQ

pionell

Sep 16, 2020

Needs adviceon

MariaDB

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

159k views159k

Comments

Detailed Comparison

Hadoop	ActiveMQ
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.	Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.
-	Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment
Statistics
GitHub Stars 15.3K	GitHub Stars 2.4K
GitHub Forks 9.1K	GitHub Forks 1.5K
Stacks 2.7K	Stacks 879
Followers 2.3K	Followers 1.3K
Votes 56	Votes 77
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Amazon aws 1 Java syntax	Pros 18 Easy to use 14 Open source 13 Efficient 10 JMS compliant 6 High Availability Cons 1 ONLY Vertically Scalable 1 Support 1 Difficult to scale 1 Low resilience to exceptions and interruptions

What are some alternatives to Hadoop, ActiveMQ?

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.

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.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

Related Comparisons

"Open source" is the top reason why over 9 developers like ActiveMQ, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.

ActiveMQ vs Hadoop