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features
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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.

Description

QikkDB is a high-performance, GPU-accelerated columnar database designed to excel in complex polygon computations and large-scale data analytics. If you're managing billions of data points and require immediate insights, qikkDB is the solution you need. It is compatible with both Windows and Linux operating systems, ensuring flexibility for developers. The project employs Google Tests for its testing framework, featuring hundreds of unit tests alongside numerous integration tests to maintain robust quality. For those developing on Windows, it is advisable to use Microsoft Visual Studio 2019, with essential dependencies that include at least CUDA version 10.2, CMake 3.15 or a more recent version, vcpkg, and Boost libraries. Meanwhile, Linux developers will also require a minimum of CUDA version 10.2, CMake 3.15 or newer, and Boost for optimal operation. This software is distributed under the Apache License, Version 2.0, allowing for a wide range of usage. To simplify the installation process, users can opt for either an installation script or a Dockerfile to get qikkDB up and running seamlessly. Additionally, this versatility makes it an appealing choice for various development environments.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

3LC Yes 
Amazon Data Firehose Yes 
Amazon SageMaker Data Wrangler Yes 
Blotout Yes 
Data Sentinel Yes 
Ficstar Yes 
Gable Yes 
Hadoop Yes 
IBM Db2 Event Store Yes 
MLJAR Studio Yes 
PuppyGraph Yes 
Querri Yes 
QuerySurge Yes 
SAS Studio Yes 
SDF Yes 
SSIS Integration Toolkit Yes 
StarfishETL Yes 
Tenzir Yes 
Tictable Yes 
Warp 10 Yes 

Integrations

3LC No 
Amazon Data Firehose No 
Amazon SageMaker Data Wrangler No 
Blotout No 
Data Sentinel No 
Ficstar No 
Gable No 
Hadoop No 
IBM Db2 Event Store No 
MLJAR Studio No 
PuppyGraph No 
Querri No 
QuerySurge No 
SAS Studio No 
SDF No 
SSIS Integration Toolkit No 
StarfishETL No 
Tenzir No 
Tictable No 
Warp 10 No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

parquet.apache.org

Vendor Details

Company Name

qikkDB

Website

github.com/qikkDB

Product Features

Product Features

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