Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Google Cloud BigQuery Reviews & Ratings
    4.5 out of 5 stars 2,027 Ratings
    Company Website for Google Cloud BigQuery
  • TinyPNG Reviews & Ratings
    4.7 out of 5 stars 72 Ratings
    Company Website for TinyPNG
  • Bacula Enterprise Reviews & Ratings
    4.5 out of 5 stars 125 Ratings
    Company Website for Bacula Enterprise
  • PDF Guru Reviews & Ratings
    4.3 out of 5 stars 101,418 Ratings
    Company Website for PDF Guru
  • CirrusPrint Reviews & Ratings
    5.0 out of 5 stars 2 Ratings
    Company Website for CirrusPrint
  • Comet Backup Reviews & Ratings
    4.5 out of 5 stars 219 Ratings
    Company Website for Comet Backup
  • MobiPDF Reviews & Ratings
    4.6 out of 5 stars 8,085 Ratings
    Company Website for MobiPDF
  • 3Q Reviews & Ratings
    4.9 out of 5 stars 14 Ratings
    Company Website for 3Q
  • Google Cloud Platform Reviews & Ratings
    4.5 out of 5 stars 61,066 Ratings
    Company Website for Google Cloud Platform
  • Kitecyber Reviews & Ratings
    4.9 out of 5 stars 87 Ratings
    Company Website for Kitecyber

What is Apache Parquet?

Parquet was created to offer the advantages of efficient and compressed columnar data formats across all initiatives within the Hadoop ecosystem. It takes into account complex nested data structures and utilizes the record shredding and assembly method described in the Dremel paper, which we consider to be a superior approach compared to just flattening nested namespaces. This format is specifically designed for maximum compression and encoding efficiency, with numerous projects demonstrating the substantial performance gains that can result from the effective use of these strategies. Parquet allows users to specify compression methods at the individual column level and is built to accommodate new encoding technologies as they arise and become accessible. Additionally, Parquet is crafted for widespread applicability, welcoming a broad spectrum of data processing frameworks within the Hadoop ecosystem without showing bias toward any particular one. By fostering interoperability and versatility, Parquet seeks to enable all users to fully harness its capabilities, enhancing their data processing tasks in various contexts. Ultimately, this commitment to inclusivity ensures that Parquet remains a valuable asset for a multitude of data-centric applications.

What is Apache Kudu?

A Kudu cluster organizes its information into tables that are similar to those in conventional relational databases. These tables can vary from simple binary key-value pairs to complex designs that contain hundreds of unique, strongly-typed attributes. Each table possesses a primary key made up of one or more columns, which may consist of a single column like a unique user ID, or a composite key such as a tuple of (host, metric, timestamp), often found in machine time-series databases. The primary key allows for quick access, modification, or deletion of rows, which ensures efficient data management. Kudu's straightforward data model simplifies the process of migrating legacy systems or developing new applications without the need to encode data into binary formats or interpret complex databases filled with hard-to-read JSON. Moreover, the tables are self-describing, enabling users to utilize widely-used tools like SQL engines or Spark for data analysis tasks. The user-friendly APIs that Kudu offers further increase its accessibility for developers. Consequently, Kudu not only streamlines data management but also preserves a solid structural integrity, making it an attractive choice for various applications. This combination of features positions Kudu as a versatile solution for modern data handling challenges.

Media

Media

Integrations Supported

Hadoop
Amazon Data Firehose
Autymate
CSViewer
Gravity Data
GribStream
Mage Sensitive Data Discovery
Meltano
OpenObserve
OrcaSheets
PuppyGraph
QStudio
SAS Studio
Semarchy xDI
Streamkap
Tenzir
Warp 10

Integrations Supported

Hadoop
Apache NiFi
Cloudera Data Warehouse
E-MapReduce

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
Linux

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

parquet.apache.org

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

kudu.apache.org/overview.html

Categories and Features

Columnar Databases

Not specified

Categories and Features

Columnar Databases

Not specified

Distributed Databases

Not specified

Popular Alternatives

Apache Iceberg Reviews & Ratings

Apache Iceberg

Apache Software Foundation

Popular Alternatives

Apache Hudi Reviews & Ratings

Apache Hudi

Apache Corporation
Apache Parquet Reviews & Ratings

Apache Parquet

The Apache Software Foundation
Apache HBase Reviews & Ratings

Apache HBase

The Apache Software Foundation
ClickHouse Reviews & Ratings

ClickHouse

5.0 out of 5 stars