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  1. Stackups
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  4. Message Queue
  5. ActiveMQ vs StreamSets

ActiveMQ vs StreamSets

OverviewComparisonAlternatives

Overview

ActiveMQ
ActiveMQ
Stacks879
Followers1.3K
Votes77
GitHub Stars2.4K
Forks1.5K
StreamSets
StreamSets
Stacks53
Followers133
Votes0

ActiveMQ vs StreamSets: 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, StreamSets is detailed as "Where DevOps Meets Data Integration". The industry's first data operations platform for full life-cycle management of data in motion.

ActiveMQ can be classified as a tool in the "Message Queue" category, while StreamSets is grouped under "Data Science Tools".

ActiveMQ is an open source tool with 1.53K GitHub stars and 1.06K GitHub forks. Here's a link to ActiveMQ's open source repository on GitHub.

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Detailed Comparison

ActiveMQ
ActiveMQ
StreamSets
StreamSets

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.

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment
Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
GitHub Stars
2.4K
GitHub Stars
-
GitHub Forks
1.5K
GitHub Forks
-
Stacks
879
Stacks
53
Followers
1.3K
Followers
133
Votes
77
Votes
0
Pros & Cons
Pros
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
Cons
  • 1
    Low resilience to exceptions and interruptions
  • 1
    ONLY Vertically Scalable
  • 1
    Support
  • 1
    Difficult to scale
Cons
  • 2
    No user community
  • 1
    Crashes
Integrations
No integrations available
HBase
HBase
Databricks
Databricks
Amazon Redshift
Amazon Redshift
MySQL
MySQL
gRPC
gRPC
Google BigQuery
Google BigQuery
Amazon Kinesis
Amazon Kinesis
Cassandra
Cassandra
Hadoop
Hadoop
Redis
Redis

What are some alternatives to ActiveMQ, StreamSets?

Kafka

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

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

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

Apache Spark

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.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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