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Description

AWS Batch provides a streamlined platform for developers, scientists, and engineers to efficiently execute vast numbers of batch computing jobs on the AWS cloud infrastructure. It automatically allocates the ideal quantity and types of compute resources, such as CPU or memory-optimized instances, tailored to the demands and specifications of the submitted batch jobs. By utilizing AWS Batch, users are spared from the hassle of installing and managing batch computing software or server clusters, enabling them to concentrate on result analysis and problem-solving. The service organizes, schedules, and manages batch workloads across a comprehensive suite of AWS compute offerings, including AWS Fargate, Amazon EC2, and Spot Instances. Importantly, there are no extra fees associated with AWS Batch itself; users only incur costs for the AWS resources, such as EC2 instances or Fargate jobs, that they deploy for executing and storing their batch jobs. This makes AWS Batch not only efficient but also cost-effective for handling large-scale computing tasks. As a result, organizations can optimize their workflows and improve productivity without being burdened by complex infrastructure management.

Description

Scale from zero to millions of events per second effortlessly. Arroyo is delivered as a single, compact binary, allowing for local development on MacOS or Linux, and seamless deployment to production environments using Docker or Kubernetes. As a pioneering stream processing engine, Arroyo has been specifically designed to simplify real-time processing, making it more accessible than traditional batch processing. Its architecture empowers anyone with SQL knowledge to create dependable, efficient, and accurate streaming pipelines. Data scientists and engineers can independently develop comprehensive real-time applications, models, and dashboards without needing a specialized team of streaming professionals. By employing SQL, users can transform, filter, aggregate, and join data streams, all while achieving sub-second response times. Your streaming pipelines should remain stable and not trigger alerts simply because Kubernetes has chosen to reschedule your pods. Built for modern, elastic cloud infrastructures, Arroyo supports everything from straightforward container runtimes like Fargate to complex, distributed setups on Kubernetes, ensuring versatility and robust performance across various environments. This innovative approach to stream processing significantly enhances the ability to manage data flows in real-time applications.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

AWS Fargate Yes 
AWS EC2 Trn3 Instances Yes 
AWS HPC Yes 
AWS ParallelCluster Yes 
AWS Secrets Manager Yes 
Amazon EC2 Yes 
Amazon FSx for Lustre Yes 
Amazon Fresh Yes 
Amazon Kinesis No 
Amazon Linux 2 Yes 
Apache Kafka No 
Confluent No 
Delta Lake No 
Docker No 
Python No 
Redis No 
Rust No 
Saagie Yes 
Stonebranch Yes 

Integrations

AWS Fargate Yes 
AWS EC2 Trn3 Instances No 
AWS HPC No 
AWS ParallelCluster No 
AWS Secrets Manager No 
Amazon EC2 No 
Amazon FSx for Lustre No 
Amazon Fresh No 
Amazon Kinesis Yes 
Amazon Linux 2 No 
Apache Kafka Yes 
Confluent Yes 
Delta Lake Yes 
Docker Yes 
Python Yes 
Redis Yes 
Rust Yes 
Saagie No 
Stonebranch No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/batch/

Vendor Details

Company Name

Arroyo

Country

United States

Website

www.arroyo.dev/

Product Features

DevOps

Approval Workflow No 
Dashboard No 
KPIs No 
Policy Management No 
Portfolio Management No 
Prioritization No 
Release Management No 
Timeline Management No 
Troubleshooting Reports No 

Alternatives

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Alternatives

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