Average Ratings 0 Ratings
Average Ratings 0 Ratings
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.
Description
The CLI tool integrates Git, Docker, Helm, and Kubernetes with any CI system to facilitate CI/CD and Giterminism. By leveraging established technologies, you can create efficient, reliable, and cohesive CI/CD pipelines. Werf simplifies the process of getting started, allowing users to implement best practices without the need to start from scratch. Not only does Werf build and deploy applications, but it also ensures that the current state of Kubernetes is continually synchronized with any changes made in Git. This tool pioneers Giterminism, using Git as a definitive source of truth and making the entire delivery process predictable and repeatable. With Werf, users have two deployment options: either converge the application from a Git commit into Kubernetes or publish the application from a Git commit to a container registry as a bundle before deploying it to Kubernetes. The setup for Werf is straightforward, requiring minimal configuration, making it accessible even to those without a background in DevOps or SRE. To assist users further, a variety of guides are available to help you deploy your application in Kubernetes quickly and effectively, enhancing the overall user experience.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Docker
Yes
Kubernetes
Yes
AWS Fargate
Yes
Amazon Kinesis
Yes
Apache Avro
Yes
Apache Flink
Yes
Apache Kafka
Yes
Apache Parquet
Yes
Confluent
Yes
Delta Lake
Yes
Integrations
Docker
Yes
Kubernetes
Yes
AWS Fargate
No
Amazon Kinesis
No
Apache Avro
No
Apache Flink
No
Apache Kafka
No
Apache Parquet
No
Confluent
No
Delta Lake
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
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
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
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)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Arroyo
Country
United States
Website
www.arroyo.dev/
Vendor Details
Company Name
Werf
Country
United States
Website
werf.io
Product Features
Product Features
Continuous Delivery
Application Lifecycle Management
No
Application Release Automation
No
Build Automation
No
Build Log
No
Change Management
No
Configuration Management
No
Continuous Deployment
No
Continuous Integration
No
Feature Toggles / Feature Flags
No
Quality Management
No
Testing Management
No