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What is Spring Cloud Data Flow?

The architecture based on microservices fosters effective handling of both streaming and batch data processing, particularly suited for environments such as Cloud Foundry and Kubernetes. By implementing Spring Cloud Data Flow, users are empowered to craft complex topologies for their data pipelines, utilizing Spring Boot applications built with the frameworks of Spring Cloud Stream or Spring Cloud Task. This robust platform addresses a wide array of data processing requirements, including ETL, data import/export, event streaming, and predictive analytics. The server component of Spring Cloud Data Flow employs Spring Cloud Deployer, which streamlines the deployment of data pipelines comprising Spring Cloud Stream or Spring Cloud Task applications onto modern infrastructures like Cloud Foundry and Kubernetes. Moreover, a thoughtfully curated collection of pre-configured starter applications for both streaming and batch processing enhances various data integration and processing needs, assisting users in their exploration and practical applications. In addition to these features, developers are given the ability to develop bespoke stream and task applications that cater to specific middleware or data services, maintaining alignment with the accessible Spring Boot programming model. This level of customization and flexibility ultimately positions Spring Cloud Data Flow as a crucial resource for organizations aiming to refine and enhance their data management workflows. Overall, its comprehensive capabilities facilitate a seamless integration of data processing tasks into everyday operations.

What is Arroyo?

Scale from zero to millions of events each second with Arroyo, which is provided as a single, efficient binary. It can be executed locally on MacOS or Linux for development needs and can be seamlessly deployed into production via Docker or Kubernetes. Arroyo offers a groundbreaking approach to stream processing that prioritizes the ease of real-time operations over conventional batch processing methods. Designed from the ground up, Arroyo enables anyone with a basic knowledge of SQL to construct reliable, efficient, and precise streaming pipelines. This capability allows data scientists and engineers to build robust real-time applications, models, and dashboards without requiring a specialized team focused on streaming. Users can easily perform operations such as transformations, filtering, aggregation, and data stream joining merely by writing SQL, achieving results in less than a second. Additionally, your streaming pipelines are insulated from triggering alerts simply due to Kubernetes deciding to reschedule your pods. With its ability to function in modern, elastic cloud environments, Arroyo caters to a range of setups from simple container runtimes like Fargate to large-scale distributed systems managed with Kubernetes. This adaptability makes Arroyo the perfect option for organizations aiming to refine their streaming data workflows, ensuring that they can efficiently handle the complexities of real-time data processing. Moreover, Arroyo’s user-friendly design helps organizations streamline their operations significantly, leading to an overall increase in productivity and innovation.

Media

Media

Integrations Supported

Kubernetes
AWS Fargate
Apache Avro
Apache Flink
Apache Kafka
Apache Parquet
Apache Tomcat
Cloud Foundry
Confluent
Delta Lake
Docker
JSON
PostgreSQL
Python
Redis
Redpanda
Rust
SQL
Spring
Spring Framework

Integrations Supported

Kubernetes
AWS Fargate
Apache Avro
Apache Flink
Apache Kafka
Apache Parquet
Apache Tomcat
Cloud Foundry
Confluent
Delta Lake
Docker
JSON
PostgreSQL
Python
Redis
Redpanda
Rust
SQL
Spring
Spring Framework

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Spring

Company Website

spring.io/projects/spring-cloud-dataflow

Company Facts

Organization Name

Arroyo

Company Location

United States

Company Website

www.arroyo.dev/

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