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  1. Stackups
  2. Application & Data
  3. Container Registry
  4. Container Tools
  5. Cloudflow vs Portainer

Cloudflow vs Portainer

OverviewComparisonAlternatives

Overview

Portainer
Portainer
Stacks506
Followers842
Votes146
Cloudflow
Cloudflow
Stacks5
Followers13
Votes0
GitHub Stars323
Forks89

Portainer vs Cloudflow: What are the differences?

Portainer: Simple management UI for Docker. Portainer is an open-source lightweight management UI which allows you to easily manage your Docker environments Portainer is available on Windows, Linux and Mac. It has never been so easy to manage Docker !; Cloudflow: *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.

Portainer belongs to "Container Tools" category of the tech stack, while Cloudflow can be primarily classified under "Big Data Tools".

Some of the features offered by Portainer are:

  • Docker management
  • Docker UI
  • Docker cluster management

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

  • 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

Cloudflow is an open source tool with 172 GitHub stars and 50 GitHub forks. Here's a link to Cloudflow's open source repository on GitHub.

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Detailed Comparison

Portainer
Portainer
Cloudflow
Cloudflow

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.

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 management; Docker UI; Docker cluster management; Swarm visualizer; Authentication; User Access Control; Docker container management; Docker service management; Docker overview; Docker console; Docker swarm status; Docker image management; Docker network management; Docker dashboard; Remote HTTP API; Automation
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
-
GitHub Stars
323
GitHub Forks
-
GitHub Forks
89
Stacks
506
Stacks
5
Followers
842
Followers
13
Votes
146
Votes
0
Pros & Cons
Pros
  • 36
    Simple
  • 27
    Great UI
  • 19
    Friendly
  • 12
    Easy to setup, gives a practical interface for Docker
  • 11
    Because it just works, super simple yet powerful
No community feedback yet
Integrations
Docker Swarm
Docker Swarm
Docker Secrets
Docker Secrets
Auth0
Auth0
Kubernetes
Kubernetes
Docker
Docker
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Akka
Akka
Apache Flink
Apache Flink

What are some alternatives to Portainer, Cloudflow?

Kubernetes

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

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 Compose

Docker Compose

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.

Docker Swarm

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

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.

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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.

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