Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Couchbase Reviews & Ratings
    4.4 out of 5 stars 418 Ratings
    Company Website for Couchbase
  • RaimaDB Reviews & Ratings
    4.8 out of 5 stars 12 Ratings
    Company Website for RaimaDB
  • Servers.com by Nexcess Reviews & Ratings
    4.8 out of 5 stars 15 Ratings
    Company Website for Servers.com by Nexcess
  • Google Cloud BigQuery Reviews & Ratings
    4.5 out of 5 stars 2,037 Ratings
    Company Website for Google Cloud BigQuery
  • JS7 JobScheduler Reviews & Ratings
    5.0 out of 5 stars 1 Rating
    Company Website for JS7 JobScheduler
  • Elecard Boro Reviews & Ratings
    4.7 out of 5 stars 9 Ratings
    Company Website for Elecard Boro
  • Kasm Workspaces Reviews & Ratings
    4.8 out of 5 stars 127 Ratings
    Company Website for Kasm Workspaces
  • 3Q Reviews & Ratings
    4.9 out of 5 stars 14 Ratings
    Company Website for 3Q
  • Hotspot Shield Reviews & Ratings
    4.2 out of 5 stars 121 Ratings
    Company Website for Hotspot Shield
  • LTX Reviews & Ratings
    4.4 out of 5 stars 182 Ratings
    Company Website for LTX

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.

What is Apache Spark?

Apache Spark™ is a powerful analytics platform crafted for large-scale data processing endeavors. It excels in both batch and streaming tasks by employing an advanced Directed Acyclic Graph (DAG) scheduler, a highly effective query optimizer, and a streamlined physical execution engine. With more than 80 high-level operators at its disposal, Spark greatly facilitates the creation of parallel applications. Users can engage with the framework through a variety of shells, including Scala, Python, R, and SQL. Spark also boasts a rich ecosystem of libraries—such as SQL and DataFrames, MLlib for machine learning, GraphX for graph analysis, and Spark Streaming for processing real-time data—which can be effortlessly woven together in a single application. This platform's versatility allows it to operate across different environments, including Hadoop, Apache Mesos, Kubernetes, standalone systems, or cloud platforms. Additionally, it can interface with numerous data sources, granting access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and many other systems, thereby offering the flexibility to accommodate a wide range of data processing requirements. Such a comprehensive array of functionalities makes Spark a vital resource for both data engineers and analysts, who rely on it for efficient data management and analysis. The combination of its capabilities ensures that users can tackle complex data challenges with greater ease and speed.

Media

Media

Integrations Supported

Delta Lake
Kubernetes
SQL
Apache Kafka
JSON
Python

Integrations Supported

Delta Lake
Kubernetes
SQL
3forge
Acxiom Real Identity
Apache Kudu
BentoML
Equalum
Gable
Molecula
Okera
Privacera
Speedb
Thunder Compute
definity
doolytic
geoblink

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Version

Supported Platforms

Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Arroyo

Company Location

United States

Company Website

www.arroyo.dev/

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

spark.apache.org

Categories and Features

Categories and Features

Big Data

Not specified

Data Analysis

Not specified

Data Modeling

Not specified

Query Engines

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation

Popular Alternatives

Popular Alternatives

TIMi Reviews & Ratings

TIMi

4.9 out of 5 stars
Snowflake Reviews & Ratings

Snowflake

4.8 out of 5 stars
dbt Reviews & Ratings

dbt

dbt Labs
5.0 out of 5 stars
MLlib Reviews & Ratings

MLlib

Apache Software Foundation