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What is Apache Phoenix?

Apache Phoenix effectively merges online transaction processing (OLTP) with operational analytics in the Hadoop ecosystem, making it suitable for applications that require low-latency responses by blending the advantages of both domains. It utilizes standard SQL and JDBC APIs while providing full ACID transaction support, as well as the flexibility of schema-on-read common in NoSQL systems through its use of HBase for storage. Furthermore, Apache Phoenix integrates effortlessly with various components of the Hadoop ecosystem, including Spark, Hive, Pig, Flume, and MapReduce, thereby establishing itself as a robust data platform for both OLTP and operational analytics through the use of widely accepted industry-standard APIs. The framework translates SQL queries into a series of HBase scans, efficiently managing these operations to produce traditional JDBC result sets. By making direct use of the HBase API and implementing coprocessors along with specific filters, Apache Phoenix delivers exceptional performance, often providing results in mere milliseconds for smaller queries and within seconds for extensive datasets that contain millions of rows. This outstanding capability positions it as an optimal solution for applications that necessitate swift data retrieval and thorough analysis, further enhancing its appeal in the field of big data processing. Its ability to handle complex queries with efficiency only adds to its reputation as a top choice for developers seeking to harness the power of Hadoop for both transactional and analytical workloads.

What is Apache Flume?

Flume serves as a powerful service tailored for the reliable, accessible, and efficient collection, aggregation, and transfer of large volumes of log data across distributed systems. Its design is both simple and flexible, relying on streaming data flows that provide robustness and fault tolerance through multiple reliability and recovery strategies. The system features a straightforward and extensible data model, making it well-suited for online analytical applications. The Apache Flume team is thrilled to announce the launch of Flume 1.8.0, which significantly boosts its capacity to handle extensive streaming event data effortlessly. This latest version promises enhanced performance and improved efficiency in the management of data flows, ultimately benefiting users in their data handling processes. Furthermore, this update reinforces Flume's commitment to evolving in response to the growing demands of data management in modern applications.

Media

Media

Integrations Supported

3forge
Amazon EMR
Apache Flume
Apache HBase
Apache Hive
Apache Phoenix
Apache Spark
Data Sentinel
Hadoop
NoSQL
Observo AI
Onum
Python
SQL
Salesforce Data 360
Trino
Yandex Data Proc

Integrations Supported

3forge
Amazon EMR
Apache Flume
Apache HBase
Apache Hive
Apache Phoenix
Apache Spark
Data Sentinel
Hadoop
NoSQL
Observo AI
Onum
Python
SQL
Salesforce Data 360
Trino
Yandex Data Proc

API Availability

Has API

API Availability

Has API

Pricing Information

Free
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

Apache Software Foundation

Company Website

phoenix.apache.org

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

flume.apache.org

Categories and Features

Relational Database

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

Categories and Features

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

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