Argo vs Cloudflow
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). | 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. |
DAG or Steps based declaration of workflows;Artifact support (S3, Artifactory, HTTP, Git, raw);Step level input & outputs (artifacts/parameters);Loops;Parameterization;Conditionals;Timeouts (step & workflow level);Retry (step & workflow level);Resubmit (memoized);Suspend & Resume;Cancellation;K8s resource orchestration;Exit Hooks (notifications, cleanup);Garbage collection of completed workflow;Scheduling (affinity/tolerations/node selectors);Volumes (ephemeral/existing);Parallelism limits;Daemoned steps;DinD (docker-in-docker);Script steps | 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 761 | Stacks 5 |
Followers 470 | Followers 13 |
Votes 6 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
What are some alternatives to Argo, 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 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
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.
