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Average Ratings 0 Ratings

Total
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

Data processing that integrates both streaming and batch operations while being serverless, efficient, and budget-friendly. It offers a fully managed service for data processing, ensuring seamless automation in the provisioning and administration of resources. With horizontal autoscaling capabilities, worker resources can be adjusted dynamically to enhance overall resource efficiency. The innovation is driven by the open-source community, particularly through the Apache Beam SDK. This platform guarantees reliable and consistent processing with exactly-once semantics. Dataflow accelerates the development of streaming data pipelines, significantly reducing data latency in the process. By adopting a serverless model, teams can devote their efforts to programming rather than the complexities of managing server clusters, effectively eliminating the operational burdens typically associated with data engineering tasks. Additionally, Dataflow’s automated resource management not only minimizes latency but also optimizes utilization, ensuring that teams can operate with maximum efficiency. Furthermore, this approach promotes a collaborative environment where developers can focus on building robust applications without the distraction of underlying infrastructure concerns.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS Fargate Yes 
Apache Kafka Yes 
Apache Parquet Yes 
CData Connect No 
Confluent Yes 
DataBuck No 
Docker Yes 
Google Cloud Datastream No 
Google Cloud Knowledge Catalog No 
Google Cloud Platform No 
Google Cloud Profiler No 
New Relic No 
Orchestra No 
Pantomath No 
Protegrity No 
Python Yes 
Rust Yes 
SQL Yes 
Sedai No 
Ternary No 

Integrations

AWS Fargate No 
Apache Kafka No 
Apache Parquet No 
CData Connect Yes 
Confluent No 
DataBuck Yes 
Docker No 
Google Cloud Datastream Yes 
Google Cloud Knowledge Catalog Yes 
Google Cloud Platform Yes 
Google Cloud Profiler Yes 
New Relic Yes 
Orchestra Yes 
Pantomath Yes 
Protegrity Yes 
Python No 
Rust No 
SQL No 
Sedai Yes 
Ternary 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) No 
Online Support No 

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

Google

Founded

1998

Country

United States

Website

cloud.google.com/dataflow

Product Features

Streaming Analytics

Data Enrichment Yes 
Data Wrangling / Data Prep Yes 
Multiple Data Source Support Yes 
Process Automation Yes 
Real-time Analysis / Reporting Yes 
Visualization Dashboards Yes 

Alternatives

Alternatives

Apache Beam Reviews

Apache Beam

Apache Software Foundation