AWS Batch vs Kubernetes

Overview

Kubernetes

Stacks61.2K

Followers52.8K

Votes685

AWS Batch

Stacks84

Followers251

Votes6

AWS Batch vs Kubernetes: What are the differences?

Introduction

In this article, we will compare AWS Batch and Kubernetes, two popular technologies for managing containerized workloads.

Scalability and Elasticity: AWS Batch is a fully managed service that automatically provisions and scales compute resources based on the workload submitted, allowing you to dynamically adjust resources as needed. In contrast, Kubernetes requires manual configuration and scaling of nodes to handle workload demands.
Job Scheduling and Management: AWS Batch provides a job queueing system that handles the scheduling and resource management for executing batch computing workloads. It allows you to define job dependencies, priorities, and constraints. Kubernetes, on the other hand, is a container orchestration platform that primarily focuses on managing and scheduling long-running services rather than batch jobs.
Container Lifecycle Management: Kubernetes has a powerful set of tools for managing the lifecycle of containers, including rolling updates, self-healing, and scaling based on metrics. AWS Batch, while it also supports containers, does not provide the same level of control and automation for container lifecycle management.
Ease of Use and Deployment: AWS Batch is a fully managed service that abstracts away the underlying infrastructure, making it easier to get started and deploy batch workloads. Kubernetes, on the other hand, requires manual setup and configuration of the cluster, which can be more complex and time-consuming.
Integration with Other Services: AWS Batch seamlessly integrates with other AWS services such as Amazon S3, DynamoDB, and CloudWatch, allowing you to easily incorporate these services into your batch jobs. While Kubernetes can also integrate with various services, it requires additional setup and configuration.
Cost Management: AWS Batch offers cost optimization features like spot instances that allow you to use available unused EC2 instances at a lower cost. Kubernetes does not provide such built-in cost management features, requiring manual optimization efforts to keep costs under control.

In summary, AWS Batch provides a fully managed solution with seamless integration to other AWS services and ease of use, while Kubernetes offers more control and flexibility in managing containerized services with powerful lifecycle management capabilities.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Kubernetes, AWS Batch

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

GitHub

GitHub Pages

Markdown

Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
Scalability: All-in-one framework for distributed systems.
Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

12.8M views12.8M

Comments

Detailed Comparison

Kubernetes	AWS Batch
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.	It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable	-
Statistics
Stacks 61.2K	Stacks 84
Followers 52.8K	Followers 251
Votes 685	Votes 6
Pros & Cons
Pros 166 Leading docker container management solution 130 Simple and powerful 108 Open source 76 Backed by google 58 The right abstractions Cons 16 Steep learning curve 15 Poor workflow for development 8 Orchestrates only infrastructure 4 High resource requirements for on-prem clusters 2 Too heavy for simple systems	Pros 3 Scalable 3 Containerized Cons 3 More overhead than lambda 1 Image management
Integrations
Vagrant Docker Rackspace Cloud Servers Microsoft Azure Google Compute Engine Ansible Google Kubernetes Engine	No integrations available

What are some alternatives to Kubernetes, AWS Batch?

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.

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

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.

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

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.

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Related Comparisons

AWS Batch vs Kubernetes: What are the differences?

Introduction

In this article, we will compare AWS Batch and Kubernetes, two popular technologies for managing containerized workloads.

Scalability and Elasticity: AWS Batch is a fully managed service that automatically provisions and scales compute resources based on the workload submitted, allowing you to dynamically adjust resources as needed. In contrast, Kubernetes requires manual configuration and scaling of nodes to handle workload demands.
Job Scheduling and Management: AWS Batch provides a job queueing system that handles the scheduling and resource management for executing batch computing workloads. It allows you to define job dependencies, priorities, and constraints. Kubernetes, on the other hand, is a container orchestration platform that primarily focuses on managing and scheduling long-running services rather than batch jobs.
Container Lifecycle Management: Kubernetes has a powerful set of tools for managing the lifecycle of containers, including rolling updates, self-healing, and scaling based on metrics. AWS Batch, while it also supports containers, does not provide the same level of control and automation for container lifecycle management.
Ease of Use and Deployment: AWS Batch is a fully managed service that abstracts away the underlying infrastructure, making it easier to get started and deploy batch workloads. Kubernetes, on the other hand, requires manual setup and configuration of the cluster, which can be more complex and time-consuming.
Integration with Other Services: AWS Batch seamlessly integrates with other AWS services such as Amazon S3, DynamoDB, and CloudWatch, allowing you to easily incorporate these services into your batch jobs. While Kubernetes can also integrate with various services, it requires additional setup and configuration.
Cost Management: AWS Batch offers cost optimization features like spot instances that allow you to use available unused EC2 instances at a lower cost. Kubernetes does not provide such built-in cost management features, requiring manual optimization efforts to keep costs under control.

AWS Batch vs Kubernetes

Overview

AWS Batch vs Kubernetes: What are the differences?

Introduction

Share your Stack

Advice on Kubernetes, AWS Batch

Detailed Comparison

What are some alternatives to Kubernetes, AWS Batch?

Rancher

Docker Compose

AWS Lambda

Docker Swarm

Tutum

Portainer

Azure Functions

Google Cloud Run

Codefresh

Serverless

Related Comparisons

Bitbucket vs GitHub vs GitLab

Bootstrap vs Materialize

Django vs Laravel vs Node.js

Bootstrap vs Foundation vs Material UI

Node.js vs Spring-Boot

AWS Batch vs Kubernetes

Overview

AWS Batch vs Kubernetes: What are the differences?

Introduction

Share your Stack

Advice on Kubernetes, AWS Batch

Detailed Comparison

What are some alternatives to Kubernetes, AWS Batch?

Rancher

Docker Compose

AWS Lambda

Docker Swarm

Tutum

Portainer

Azure Functions

Google Cloud Run

Codefresh

Serverless

Related Comparisons

Bitbucket vs GitHub vs GitLab

Bootstrap vs Materialize

Django vs Laravel vs Node.js

Bootstrap vs Foundation vs Material UI

Node.js vs Spring-Boot