Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Description

Hudi serves as a robust platform for constructing streaming data lakes equipped with incremental data pipelines, all while utilizing a self-managing database layer that is finely tuned for lake engines and conventional batch processing. It effectively keeps a timeline of every action taken on the table at various moments, enabling immediate views of the data while also facilitating the efficient retrieval of records in the order they were received. Each Hudi instant is composed of several essential components, allowing for streamlined operations. The platform excels in performing efficient upserts by consistently linking a specific hoodie key to a corresponding file ID through an indexing system. This relationship between record key and file group or file ID remains constant once the initial version of a record is written to a file, ensuring stability in data management. Consequently, the designated file group encompasses all iterations of a collection of records, allowing for seamless data versioning and retrieval. This design enhances both the reliability and efficiency of data operations within the Hudi ecosystem.

Description

A Kudu cluster comprises tables that resemble those found in traditional relational (SQL) databases. These tables can range from a straightforward binary key and value structure to intricate designs featuring hundreds of strongly-typed attributes. Similar to SQL tables, each Kudu table is defined by a primary key, which consists of one or more columns; this could be a single unique user identifier or a composite key such as a (host, metric, timestamp) combination tailored for time-series data from machines. The primary key allows for quick reading, updating, or deletion of rows. The straightforward data model of Kudu facilitates the migration of legacy applications as well as the development of new ones, eliminating concerns about encoding data into binary formats or navigating through cumbersome JSON databases. Additionally, tables in Kudu are self-describing, enabling the use of standard analysis tools like SQL engines or Spark. With user-friendly APIs, Kudu ensures that developers can easily integrate and manipulate their data. This approach not only streamlines data management but also enhances overall efficiency in data processing tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Apache Flink
Apache Spark
Hadoop
Alluxio
Amazon Athena
Apache Cassandra
Apache Doris
Apache Hive
Apache Kafka
Apache NiFi
Azure Data Lake
BigBI
DataHub
E-MapReduce
MySQL
Onehouse
PostgreSQL
Presto
PuppyGraph
e6data

Integrations

Apache Flink
Apache Spark
Hadoop
Alluxio
Amazon Athena
Apache Cassandra
Apache Doris
Apache Hive
Apache Kafka
Apache NiFi
Azure Data Lake
BigBI
DataHub
E-MapReduce
MySQL
Onehouse
PostgreSQL
Presto
PuppyGraph
e6data

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 Corporation

Founded

1954

Country

United States

Website

hudi.apache.org

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

kudu.apache.org/overview.html

Product Features

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

Alternatives

Alternatives

Apache Parquet Reviews

Apache Parquet

The Apache Software Foundation
Apache Iceberg Reviews

Apache Iceberg

Apache Software Foundation
Apache Hudi Reviews

Apache Hudi

Apache Corporation
Apache Doris Reviews

Apache Doris

The Apache Software Foundation
Apache HBase Reviews

Apache HBase

The Apache Software Foundation