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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Pinot is built to efficiently handle OLAP queries on static data with minimal latency. It incorporates various pluggable indexing methods, including Sorted Index, Bitmap Index, and Inverted Index. While it currently lacks support for joins, this limitation can be mitigated by utilizing Trino or PrestoDB for querying purposes. The system offers an SQL-like language that enables selection, aggregation, filtering, grouping, ordering, and distinct queries on datasets. It comprises both offline and real-time tables, with real-time tables being utilized to address segments lacking offline data. Additionally, users can tailor the anomaly detection process and notification mechanisms to accurately identify anomalies. This flexibility ensures that users can maintain data integrity and respond proactively to potential issues.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Apache Kafka
Amazon Kinesis
Amazon S3
Apache Avro
Apache Flink
Apache Parquet
Astro by Astronomer
Confluent
Delta Lake
Hadoop
Hue
JSON
Kestra
Kubernetes
OpenMetadata
PostgreSQL
Python
Redpanda
Rust
SQL

Integrations

Apache Kafka
Amazon Kinesis
Amazon S3
Apache Avro
Apache Flink
Apache Parquet
Astro by Astronomer
Confluent
Delta Lake
Hadoop
Hue
JSON
Kestra
Kubernetes
OpenMetadata
PostgreSQL
Python
Redpanda
Rust
SQL

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Apache Corporation

Founded

1954

Country

United Statess

Website

pinot.apache.org

Vendor Details

Company Name

Arroyo

Country

United States

Website

www.arroyo.dev/

Alternatives

Alternatives

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