Cloudflow vs Kylo

Overview

Kylo

Stacks15

Followers40

Votes0

GitHub Stars1.1K

Forks571

Cloudflow

Stacks5

Followers13

Votes0

GitHub Stars323

Forks89

Cloudflow vs Kylo: What are the differences?

Deployment Flexibility: Cloudflow provides the flexibility to deploy applications on any Kubernetes cluster, while Kylo is more focused on deploying applications on Apache Hadoop clusters, particularly with Apache NiFi.
Data Processing Capabilities: Cloudflow emphasizes building streaming data pipelines using technologies like Akka Streams and Apache Kafka, whereas Kylo focuses on data ingestion and preparation workflows using Apache NiFi and other related tools.
Monitoring and Management: Cloudflow offers built-in monitoring and management features for applications deployed on Kubernetes, leveraging tools like Prometheus and Grafana, whereas Kylo relies on third-party solutions for monitoring and management of data workflows.
Scalability and Performance: Cloudflow is designed for high scalability and performance, leveraging Kubernetes' capabilities for horizontal scaling and resource optimization, while Kylo's focus is more on simplifying data workflows rather than high performance computing.
Community and Support: Cloudflow has a growing community of developers and users contributing to its ecosystem, providing a robust support network, whereas Kylo may have a smaller community base and limited support resources.
Integration Capabilities: Cloudflow offers seamless integration with various data sources and sinks, including cloud services, databases, and messaging systems, while Kylo's integrations are more centered around Hadoop ecosystem tools and technologies.

In Summary, Cloudflow and Kylo differ in terms of deployment flexibility, data processing capabilities, monitoring and management features, scalability, community support, and integration capabilities.

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Detailed Comparison

Kylo	Cloudflow
It is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects.	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.
Self-service data ingest with data cleansing, validation, and automatic profiling; Wrangle data with visual sql and an interactive transform through a simple user interface; Search and explore data and metadata, view lineage, and profile statistics; Monitor health of feeds and services in the data lake. Track SLAs and troubleshoot performance	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 1.1K	GitHub Stars 323
GitHub Forks 571	GitHub Forks 89
Stacks 15	Stacks 5
Followers 40	Followers 13
Votes 0	Votes 0
Integrations
ActiveMQ Apache Spark Hadoop Apache NiFi	Kubernetes Apache Spark Akka Apache Flink

What are some alternatives to Kylo, 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.

Related Comparisons

Cloudflow vs Kylo: What are the differences?

Deployment Flexibility: Cloudflow provides the flexibility to deploy applications on any Kubernetes cluster, while Kylo is more focused on deploying applications on Apache Hadoop clusters, particularly with Apache NiFi.
Data Processing Capabilities: Cloudflow emphasizes building streaming data pipelines using technologies like Akka Streams and Apache Kafka, whereas Kylo focuses on data ingestion and preparation workflows using Apache NiFi and other related tools.
Monitoring and Management: Cloudflow offers built-in monitoring and management features for applications deployed on Kubernetes, leveraging tools like Prometheus and Grafana, whereas Kylo relies on third-party solutions for monitoring and management of data workflows.
Scalability and Performance: Cloudflow is designed for high scalability and performance, leveraging Kubernetes' capabilities for horizontal scaling and resource optimization, while Kylo's focus is more on simplifying data workflows rather than high performance computing.
Community and Support: Cloudflow has a growing community of developers and users contributing to its ecosystem, providing a robust support network, whereas Kylo may have a smaller community base and limited support resources.
Integration Capabilities: Cloudflow offers seamless integration with various data sources and sinks, including cloud services, databases, and messaging systems, while Kylo's integrations are more centered around Hadoop ecosystem tools and technologies.

Cloudflow vs Kylo

Overview