NSQ vs StreamSets

Overview

NSQ

Stacks142

Followers356

Votes148

StreamSets

Stacks53

Followers133

Votes0

NSQ vs StreamSets: What are the differences?

NSQ: A realtime distributed messaging platform. 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; StreamSets: Where DevOps Meets Data Integration. The industry's first data operations platform for full life-cycle management of data in motion.

NSQ and StreamSets are primarily classified as "Message Queue" and "Data Science" tools respectively.

Some of the features offered by NSQ are:

support distributed topologies with no SPOF
horizontally scalable (no brokers, seamlessly add more nodes to the cluster)
low-latency push based message delivery (performance)

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

NSQ is an open source tool with 15.9K GitHub stars and 2.07K GitHub forks. Here's a link to NSQ's open source repository on GitHub.

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Advice on NSQ, StreamSets

Pramod

Co Founder at Usability Designs

Mar 2, 2020

Needs advice

I am looking into IoT World Solution where we have MQTT Broker. This MQTT Broker Sits in one of the Data Center. We are doing a lot of Alert and Alarm related processing on that Data, Currently, we are looking into Solution which can do distributed persistence of log/alert primarily on remote Disk.

Our primary need is to use lightweight where operational complexity and maintenance costs can be significantly reduced. We want to do it on-premise so we are not considering cloud solutions.

We looked into the following alternatives:

Apache Kafka - Great choice but operation and maintenance wise very complex. Rabbit MQ - High availability is the issue, Apache Pulsar - Operational Complexity. NATS - Absence of persistence. Akka Streams - Big learning curve and operational streams.

So we are looking into a lightweight library that can do distributed persistence preferably with publisher and subscriber model. Preferable on JVM stack.

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Comments

Detailed Comparison

NSQ	StreamSets
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.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
support distributed topologies with no SPOF;horizontally scalable (no brokers, seamlessly add more nodes to the cluster);low-latency push based message delivery (performance);combination load-balanced and multicast style message routing;excel at both streaming (high-throughput) and job oriented (low-throughput) workloads;primarily in-memory (beyond a high-water mark messages are transparently kept on disk);runtime discovery service for consumers to find producers (nsqlookupd);transport layer security (TLS);data format agnostic;few dependencies (easy to deploy) and a sane, bounded, default configuration;simple TCP protocol supporting client libraries in any language;HTTP interface for stats, admin actions, and producers (no client library needed to publish);integrates with statsd for realtime instrumentation;robust cluster administration interface (nsqadmin)	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 142	Stacks 53
Followers 356	Followers 133
Votes 148	Votes 0
Pros & Cons
Pros 29 It's in golang 20 Distributed 20 Lightweight 18 Easy setup 17 High throughput Cons 1 Long term persistence 1 Get NSQ behavior out of Kafka but not inverse 1 HA	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 NSQ, 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.

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.

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.

Related Comparisons

Some of the features offered by NSQ are:

support distributed topologies with no SPOF
horizontally scalable (no brokers, seamlessly add more nodes to the cluster)
low-latency push based message delivery (performance)

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

NSQ is an open source tool with 15.9K GitHub stars and 2.07K GitHub forks. Here's a link to NSQ's open source repository on GitHub.

NSQ vs StreamSets