MSMQ vs StreamSets | What are the differences? | StackShare

MSMQ vs StreamSets

Overview

MSMQ

Stacks33

Followers118

Votes3

StreamSets

Stacks53

Followers133

Votes0

MSMQ vs StreamSets: What are the differences?

MSMQ: A technology for asynchronous messaging. This technology enables applications running at different times to communicate across heterogeneous networks and systems that may be temporarily offline. Applications send messages to queues and read messages from queues; StreamSets: Where DevOps Meets Data Integration. The industry's first data operations platform for full life-cycle management of data in motion.

MSMQ belongs to "Message Queue" category of the tech stack, while StreamSets can be primarily classified under "Data Science Tools".

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Detailed Comparison

MSMQ	StreamSets
This technology enables applications running at different times to communicate across heterogeneous networks and systems that may be temporarily offline. Applications send messages to queues and read messages from queues.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
-	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 33	Stacks 53
Followers 118	Followers 133
Votes 3	Votes 0
Pros & Cons
Pros 2 Easy to learn 1 Cloud not needed Cons 1 Windows dependency	Cons 2 No user community 1 Crashes
Integrations
No integrations available	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to MSMQ, 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.