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  4. Stream Processing
  5. Apache Storm vs Humanify

Apache Storm vs Humanify

OverviewComparisonAlternatives

Overview

Apache Storm
Apache Storm
Stacks208
Followers282
Votes25
GitHub Stars6.7K
Forks4.1K
Humanify
Humanify
Stacks0
Followers1
Votes0
GitHub Stars7
Forks1

Apache Storm vs Humanify: What are the differences?

Developers describe Apache Storm as "Distributed and fault-tolerant realtime computation". Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. On the other hand, Humanify is detailed as "Add human touch to otherwise very machined node.js streams". It is a free and open source server and web application, written in Node.js, that allows adding human intelligence to data streaming in scenarios where computers are not suitable to make educated enough choices In just a couple lines of code it will ingest your data stream, open an HTTP server with a WebApplication that will be fed with all the data from the stream. Now you and your team can add decisions to each item of your data stream..

Apache Storm and Humanify can be primarily classified as "Stream Processing" tools.

Some of the features offered by Apache Storm are:

  • Storm integrates with the queueing and database technologies you already use
  • Simple API
  • Scalable

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

  • Simple installation
  • Fast data review
  • Human in the loop input

Apache Storm is an open source tool with 6.08K GitHub stars and 4.05K GitHub forks. Here's a link to Apache Storm's open source repository on GitHub.

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Detailed Comparison

Apache Storm
Apache Storm
Humanify
Humanify

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

It is a free and open source server and web application, written in Node.js, that allows adding human intelligence to data streaming in scenarios where computers are not suitable to make educated enough choices. In just a couple lines of code it will ingest your data stream, open an HTTP server with a WebApplication that will be fed with all the data from the stream. Now you and your team can add decisions to each item of your data stream.

Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source
Simple installation; Fast data review; Human in the loop input; Open source
Statistics
GitHub Stars
6.7K
GitHub Stars
7
GitHub Forks
4.1K
GitHub Forks
1
Stacks
208
Stacks
0
Followers
282
Followers
1
Votes
25
Votes
0
Pros & Cons
Pros
  • 10
    Flexible
  • 6
    Easy setup
  • 4
    Event Processing
  • 3
    Clojure
  • 2
    Real Time
No community feedback yet
Integrations
No integrations available
Node.js
Node.js

What are some alternatives to Apache Storm, Humanify?

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.

Confluent

Confluent

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

KSQL

KSQL

KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.

Heron

Heron

Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.

Kafka Streams

Kafka Streams

It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.

Kapacitor

Kapacitor

It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.

Redpanda

Redpanda

It is a streaming platform for mission critical workloads. Kafka® compatible, No Zookeeper®, no JVM, and no code changes required. Use all your favorite open source tooling - 10x faster.

Faust

Faust

It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.

Samza

Samza

It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

Benthos

Benthos

It is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads.

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