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ease
features
design
support

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Description

Apache Phoenix provides low-latency OLTP and operational analytics on Hadoop by merging the advantages of traditional SQL with the flexibility of NoSQL. It utilizes HBase as its underlying storage, offering full ACID transaction support alongside late-bound, schema-on-read capabilities. Fully compatible with other Hadoop ecosystem tools such as Spark, Hive, Pig, Flume, and MapReduce, it establishes itself as a reliable data platform for OLTP and operational analytics through well-defined, industry-standard APIs. When a SQL query is executed, Apache Phoenix converts it into a series of HBase scans, managing these scans to deliver standard JDBC result sets seamlessly. The framework's direct interaction with the HBase API, along with the implementation of coprocessors and custom filters, enables performance metrics that can reach milliseconds for simple queries and seconds for larger datasets containing tens of millions of rows. This efficiency positions Apache Phoenix as a formidable choice for businesses looking to enhance their data processing capabilities in a Big Data environment.

Description

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Python
APIFuzzer
Apache Spark
CoLab
Codestral Mamba
Conda
CrewAI
Data Sentinel
Databricks
GitHub
Guardrails AI
Haystack
JupyterLab
LlamaIndex
Mistral Large
Mistral Small
Mixtral 8x22B
OpenAI
OpenTelemetry
Slack

Integrations

Python
APIFuzzer
Apache Spark
CoLab
Codestral Mamba
Conda
CrewAI
Data Sentinel
Databricks
GitHub
Guardrails AI
Haystack
JupyterLab
LlamaIndex
Mistral Large
Mistral Small
Mixtral 8x22B
OpenAI
OpenTelemetry
Slack

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
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 Software Foundation

Website

phoenix.apache.org

Vendor Details

Company Name

Arize AI

Country

United States

Website

docs.arize.com/phoenix

Product Features

Relational Database

ACID Compliance
Data Failure Recovery
Multi-Platform
Referential Integrity
SQL DDL Support
SQL DML Support
System Catalog
Unicode Support

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