IronMQ vs StreamSets

Overview

IronMQ

Stacks35

Followers49

Votes36

StreamSets

Stacks53

Followers133

Votes0

IronMQ vs StreamSets: What are the differences?

What is IronMQ? Message Queue for any deployment. An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

What is StreamSets? Where DevOps Meets Data Integration. The industry's first data operations platform for full life-cycle management of data in motion.

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

Some of the features offered by IronMQ are:

Instant High Availability- Runs on top cloud infrastructures and uses multiple high-availability data centers. Uses reliable datastores for message durability and persistence.
Easy to Use- IronMQ is super easy to use. Simply connect directly to the API endpoints and you're ready to create and use queues. There are also client libraries available in any language you want – Ruby, Python, PHP, Java, .NET, Go, Node.JS, and more
Scalable / High Performance- Built using high-performance languages designed for concurrency and runs on industrial-strength clouds. Push messages and stream data at will without worrying about memory limits or adding more servers.

On the other hand, StreamSets provides the following key features:

Build Batch & Streaming Pipelines in Hours
Map and Monitor Runtime Performance
Protect Sensitive Data as it Arrives

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Database-backed active job backend

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Detailed Comparison

IronMQ	StreamSets
An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
Instant High Availability- Runs on top cloud infrastructures and uses multiple high-availability data centers. Uses reliable datastores for message durability and persistence.;Easy to Use- IronMQ is super easy to use. Simply connect directly to the API endpoints and you're ready to create and use queues. There are also client libraries available in any language you want – Ruby, Python, PHP, Java, .NET, Go, Node.JS, and more;Scalable / High Performance- Built using high-performance languages designed for concurrency and runs on industrial-strength clouds. Push messages and stream data at will without worrying about memory limits or adding more servers.;Realtime Monitoring- Get realtime monitoring of your message queues through IronMQ's beautiful dashboard. This allows you to quickly find, diagnose, and resolve problems before others notice.;One-time FIFO delivery;Push Queues and publish-subscribe support;Queue messages using webhooks	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
Stacks 35	Stacks 53
Followers 49	Followers 133
Votes 36	Votes 0
Pros & Cons
Pros 12 Great Support 8 Heroku Add-on 3 Push support 3 Delayed delivery upto 7 days 2 Language agnostic Cons 1 Can't use rabbitmqadmin	Cons 2 No user community 1 Crashes
Integrations
Amazon EC2 Heroku Engine Yard Cloud Rackspace Cloud Servers Red Hat OpenShift StackMob AppFog cloudControl	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to IronMQ, StreamSets?

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.

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

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 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

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.

ActiveMQ

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.

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

Distributed SQL Query Engine for Big Data

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.