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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

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

Microservices architecture enables efficient streaming and batch data processing specifically designed for platforms like Cloud Foundry and Kubernetes. By utilizing Spring Cloud Data Flow, users can effectively design intricate topologies for their data pipelines, which feature Spring Boot applications developed with the Spring Cloud Stream or Spring Cloud Task frameworks. This powerful tool caters to a variety of data processing needs, encompassing areas such as ETL, data import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server leverages Spring Cloud Deployer to facilitate the deployment of these data pipelines, which consist of Spring Cloud Stream or Spring Cloud Task applications, onto contemporary infrastructures like Cloud Foundry and Kubernetes. Additionally, a curated selection of pre-built starter applications for streaming and batch tasks supports diverse data integration and processing scenarios, aiding users in their learning and experimentation endeavors. Furthermore, developers have the flexibility to create custom stream and task applications tailored to specific middleware or data services, all while adhering to the user-friendly Spring Boot programming model. This adaptability makes Spring Cloud Data Flow a valuable asset for organizations looking to optimize their data workflows.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Kubernetes Yes 
AWS Fargate Yes 
Amazon Kinesis Yes 
Apache Flink Yes 
Apache Kafka Yes 
Apache Parquet Yes 
Apache Tomcat No 
Cloud Foundry No 
Confluent Yes 
Delta Lake Yes 
Docker Yes 
JSON Yes 
Mariner Financial Wellness No 
PostgreSQL Yes 
Python Yes 
Redis Yes 
Rust Yes 
SQL Yes 
Spring Framework No 
VMware Cloud No 

Integrations

Kubernetes Yes 
AWS Fargate No 
Amazon Kinesis No 
Apache Flink No 
Apache Kafka No 
Apache Parquet No 
Apache Tomcat Yes 
Cloud Foundry Yes 
Confluent No 
Delta Lake No 
Docker No 
JSON No 
Mariner Financial Wellness Yes 
PostgreSQL No 
Python No 
Redis No 
Rust No 
SQL No 
Spring Framework Yes 
VMware Cloud Yes 

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

Customer Support

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

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
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 Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Arroyo

Country

United States

Website

www.arroyo.dev/

Vendor Details

Company Name

Spring

Website

spring.io/projects/spring-cloud-dataflow

Alternatives

Alternatives

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