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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.

Description

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Hadoop
Amazon SageMaker Data Wrangler
Apache NiFi
Apache Spark
Blotout
Data Sentinel
E-MapReduce
Flyte
Gable
Gravity Data
IBM Db2 Event Store
Mage Sensitive Data Discovery
PI.EXCHANGE
PuppyGraph
Semarchy xDI
StarfishETL
Tad
Tenzir
Timeplus
Tonic Ephemeral

Integrations

Hadoop
Amazon SageMaker Data Wrangler
Apache NiFi
Apache Spark
Blotout
Data Sentinel
E-MapReduce
Flyte
Gable
Gravity Data
IBM Db2 Event Store
Mage Sensitive Data Discovery
PI.EXCHANGE
PuppyGraph
Semarchy xDI
StarfishETL
Tad
Tenzir
Timeplus
Tonic Ephemeral

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

The Apache Software Foundation

Founded

1999

Country

United States

Website

kudu.apache.org/overview.html

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

parquet.apache.org

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