Cloudflow vs Docker Compose

Overview

Docker Compose

Stacks22.3K

Followers16.5K

Votes501

GitHub Stars36.4K

Forks5.5K

Cloudflow

Stacks5

Followers13

Votes0

GitHub Stars323

Forks89

Docker Compose vs Cloudflow: What are the differences?

Developers describe Docker Compose as "Define and run multi-container applications with Docker". With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running. On the other hand, Cloudflow is detailed as "*Streaming Data Pipeline on Kubernetes *". It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—reducing the time required to create, package, and deploy—from weeks to hours.

Docker Compose can be classified as a tool in the "Container Tools" category, while Cloudflow is grouped under "Big Data Tools".

Docker Compose and Cloudflow are both open source tools. Docker Compose with 19.3K GitHub stars and 3.08K forks on GitHub appears to be more popular than Cloudflow with 172 GitHub stars and 50 GitHub forks.

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Detailed Comparison

Docker Compose	Cloudflow
With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.	It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—reducing the time required to create, package, and deploy—from weeks to hours.
-	Apache Spark, Apache Flink, and Akka Streams; Focus only on business logic, leave the boilerplate to us; We provide all the tooling for going from business logic to a deployable Docker image; We provide Kubernetes tooling to deploy your distributed system with a single command, and manage durable connections between processing stages; With a Lightbend subscription, you get all the tools you need to provide insights, observability, and lifecycle management for evolving your distributed streaming application
Statistics
GitHub Stars 36.4K	GitHub Stars 323
GitHub Forks 5.5K	GitHub Forks 89
Stacks 22.3K	Stacks 5
Followers 16.5K	Followers 13
Votes 501	Votes 0
Pros & Cons
Pros 123 Multi-container descriptor 110 Fast development environment setup 79 Easy linking of containers 68 Simple yaml configuration 60 Easy setup Cons 9 Tied to single machine 5 Still very volatile, changing syntax often	No community feedback yet
Integrations
Docker	Kubernetes Apache Spark Akka Apache Flink

What are some alternatives to Docker Compose, Cloudflow?

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

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.

Presto

Distributed SQL Query Engine for Big Data

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.

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.