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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
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
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
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