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What is Apache Hudi?

Hudi is a versatile framework designed for the development of streaming data lakes, which seamlessly integrates incremental data pipelines within a self-managing database context, while also catering to lake engines and traditional batch processing methods. This platform maintains a detailed historical timeline that captures all operations performed on the table, allowing for real-time data views and efficient retrieval based on the sequence of arrival. Each Hudi instant is comprised of several critical components that bolster its capabilities. Hudi stands out in executing effective upserts by maintaining a direct link between a specific hoodie key and a file ID through a sophisticated indexing framework. This connection between the record key and the file group or file ID remains intact after the original version of a record is written, ensuring a stable reference point. Essentially, the associated file group contains all iterations of a set of records, enabling effortless management and access to data over its lifespan. This consistent mapping not only boosts performance but also streamlines the overall data management process, making it considerably more efficient. Consequently, Hudi's design provides users with the tools necessary for both immediate data access and long-term data integrity.

What is Apache Hive?

Apache Hive serves as a data warehousing framework that empowers users to access, manipulate, and oversee large datasets spread across distributed systems using a SQL-like language. It facilitates the structuring of pre-existing data stored in various formats. Users have the option to interact with Hive through a command line interface or a JDBC driver. As a project under the auspices of the Apache Software Foundation, Apache Hive is continually supported by a group of dedicated volunteers. Originally integrated into the Apache® Hadoop® ecosystem, it has matured into a fully-fledged top-level project with its own identity. We encourage individuals to delve deeper into the project and contribute their expertise. To perform SQL operations on distributed datasets, conventional SQL queries must be run through the MapReduce Java API. However, Hive streamlines this task by providing a SQL abstraction, allowing users to execute queries in the form of HiveQL, thus eliminating the need for low-level Java API implementations. This results in a much more user-friendly and efficient experience for those accustomed to SQL, leading to greater productivity when dealing with vast amounts of data. Moreover, the adaptability of Hive makes it a valuable tool for a diverse range of data processing tasks.

Media

Media

Integrations Supported

Apache Doris
Apache Spark
CelerData Cloud
DataHub
e6data
Apache Impala
Apache Ranger
Ascend
Data Virtuality
E-MapReduce
IRI CoSort
Inferyx
Protegrity
Rational BI
SQL
Stackable
Timbr.ai
Union Cloud
WINDEV

Integrations Supported

Apache Doris
Apache Spark
CelerData Cloud
DataHub
e6data
Apache Impala
Apache Ranger
Ascend
Data Virtuality
E-MapReduce
IRI CoSort
Inferyx
Protegrity
Rational BI
SQL
Stackable
Timbr.ai
Union Cloud
WINDEV

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
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 Corporation

Date Founded

1954

Company Location

United States

Company Website

hudi.apache.org

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

hive.apache.org

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

Categories and Features

ETL

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

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