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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
Addressing issues of data accuracy and alignment is essential to provide consumers with a more profound insight into their data landscape. By implementing a flexible and feature-rich business intelligence and analytics platform, organizations can gain the capabilities necessary to design intricate dashboards and reports tailored to user requirements. Collaborating with a company that prioritizes customer needs can empower your business to establish a sustainable competitive edge in the market. With the ability to connect to various open data sources—ranging from conventional databases and flat files to Excel sheets and web-based data via APIs—users can seamlessly integrate information. Incorporating advanced features such as self-service capabilities, data exploration, and external administrative functionalities enhances user experience. Data can be visualized through an extensive array of chart types, or customized visual representations can be created using scorecards and small multiples. Furthermore, seamless connectivity to diverse data repositories, including cloud data warehouses, Hadoop, NoSQL document stores, streaming data, and search engines, allows for comprehensive data management and analysis. This holistic approach not only improves data interaction but also fosters a culture of informed decision-making within the organization.
API Access
Has API
API Access
Has API
Integrations
Amazon EMR
Apache Flume
Apache HBase
Apache Hive
Apache Spark
Data Sentinel
Hadoop
NoSQL
Python
SQL
Integrations
Amazon EMR
Apache Flume
Apache HBase
Apache Hive
Apache Spark
Data Sentinel
Hadoop
NoSQL
Python
SQL
Pricing Details
Free
Free Trial
Free Version
Pricing Details
$20 per month
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
insightsoftware
Country
United States
Website
insightsoftware.com/logi-analytics/logi-symphony/
Product Features
Relational Database
ACID Compliance
Data Failure Recovery
Multi-Platform
Referential Integrity
SQL DDL Support
SQL DML Support
System Catalog
Unicode Support
Product Features
Business Intelligence
Ad Hoc Reports
Benchmarking
Budgeting & Forecasting
Dashboard
Data Analysis
Key Performance Indicators
Natural Language Generation (NLG)
Performance Metrics
Predictive Analytics
Profitability Analysis
Strategic Planning
Trend / Problem Indicators
Visual Analytics