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Average Ratings 0 Ratings

Total
ease
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Description

Apache Hive is a data warehouse solution that enables the efficient reading, writing, and management of substantial datasets stored across distributed systems using SQL. It allows users to apply structure to pre-existing data in storage. To facilitate user access, it comes equipped with a command line interface and a JDBC driver. As an open-source initiative, Apache Hive is maintained by dedicated volunteers at the Apache Software Foundation. Initially part of the Apache® Hadoop® ecosystem, it has since evolved into an independent top-level project. We invite you to explore the project further and share your knowledge to enhance its development. Users typically implement traditional SQL queries through the MapReduce Java API, which can complicate the execution of SQL applications on distributed data. However, Hive simplifies this process by offering a SQL abstraction that allows for the integration of SQL-like queries, known as HiveQL, into the underlying Java framework, eliminating the need to delve into the complexities of the low-level Java API. This makes working with large datasets more accessible and efficient for developers.

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.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

3forge Yes 
Amazon EMR Yes 
Apache Spark Yes 
Data Sentinel Yes 
SQL Yes 
Salesforce Data 360 Yes 
Algonomy Yes 
Apache Hudi Yes 
Apache Impala Yes 
Baidu Sugar Yes 
ClicData Yes 
Cloudera Data Warehouse Yes 
Coginiti Yes 
Flyte Yes 
IBM watsonx.data Yes 
Mage Dynamic Data Masking Yes 
Trino No 
Truedat Yes 
Union Cloud Yes 
Varada Yes 

Integrations

3forge Yes 
Amazon EMR Yes 
Apache Spark Yes 
Data Sentinel Yes 
SQL Yes 
Salesforce Data 360 Yes 
Algonomy No 
Apache Hudi No 
Apache Impala No 
Baidu Sugar No 
ClicData No 
Cloudera Data Warehouse No 
Coginiti No 
Flyte No 
IBM watsonx.data No 
Mage Dynamic Data Masking No 
Trino Yes 
Truedat No 
Union Cloud No 
Varada No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Apache Software Foundation

Founded

1999

Country

United States

Website

hive.apache.org

Vendor Details

Company Name

Apache Software Foundation

Website

phoenix.apache.org

Product Features

ETL

Data Analysis No 
Data Filtering No 
Data Quality Control No 
Job Scheduling No 
Match & Merge No 
Metadata Management No 
Non-Relational Transformations No 
Version Control No 

Product Features

Relational Database

ACID Compliance No 
Data Failure Recovery No 
Multi-Platform No 
Referential Integrity No 
SQL DDL Support No 
SQL DML Support No 
System Catalog No 
Unicode Support No 

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