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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 Kylin™ is a distributed, open-source Analytical Data Warehouse designed for Big Data, aimed at delivering OLAP (Online Analytical Processing) capabilities in the modern big data landscape. By enhancing multi-dimensional cube technology and precalculation methods on platforms like Hadoop and Spark, Kylin maintains a consistent query performance, even as data volumes continue to expand. This innovation reduces query response times from several minutes to just milliseconds, effectively reintroducing online analytics into the realm of big data. Capable of processing over 10 billion rows in under a second, Kylin eliminates the delays previously associated with report generation, facilitating timely decision-making. It seamlessly integrates data stored on Hadoop with popular BI tools such as Tableau, PowerBI/Excel, MSTR, QlikSense, Hue, and SuperSet, significantly accelerating business intelligence operations on Hadoop. As a robust Analytical Data Warehouse, Kylin supports ANSI SQL queries on Hadoop/Spark and encompasses a wide array of ANSI SQL functions. Moreover, Kylin’s architecture allows it to handle thousands of simultaneous interactive queries with minimal resource usage, ensuring efficient analytics even under heavy loads. This efficiency positions Kylin as an essential tool for organizations seeking to leverage their data for strategic insights.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Apache Spark
Astro by Astronomer
Chat2DB
Hue
RATH
Alteryx
Amazon EMR
Apache Sentry
Apache Superset
Ataccama ONE
Data Sentinel
Flyte
Immuta
Inferyx
Omniscope Evo
OpenText Structured Data Manager
Rational BI
Rocket Data Replicate & Sync
Varada
Vaultspeed

Integrations

Apache Spark
Astro by Astronomer
Chat2DB
Hue
RATH
Alteryx
Amazon EMR
Apache Sentry
Apache Superset
Ataccama ONE
Data Sentinel
Flyte
Immuta
Inferyx
Omniscope Evo
OpenText Structured Data Manager
Rational BI
Rocket Data Replicate & Sync
Varada
Vaultspeed

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
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

Founded

1999

Country

United States

Website

hive.apache.org

Vendor Details

Company Name

Apache Software Foundation

Founded

1999

Country

United States

Website

kylin.apache.org

Product Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Product 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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