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What is VelocityDB?

VelocityDB distinguishes itself as an exceptional database engine, celebrated for its capacity to store data with unparalleled speed and efficiency compared to rival solutions, all while maintaining a remarkably low cost. In contrast to conventional databases that depend on structured mappings to tables, JSON, or XML formats, it preserves .NET objects in their original state. Additionally, VelocityGraph, an open-source property graph database, works in perfect harmony with the VelocityDB object database. As a C# .NET noSQL object database, VelocityDB, when paired with VelocityGraph, emerges as one of the fastest, most scalable, and versatile database alternatives available today. The development team's commitment to addressing bugs is evident, as issues reported with reproducible test cases are generally resolved within a week, highlighting their dedication to quality. A key benefit of utilizing this database system is its outstanding adaptability, allowing developers to customize their applications to fulfill even the most particular specifications. With VelocityDB, users can choose the most appropriate data structures for their requirements, providing them with control over data's persistent placement, indexing, and access methods. This remarkable degree of customization not only enhances the user experience but also firmly establishes VelocityDB as a leader in the field of database technology, making it an attractive option for developers seeking efficiency and flexibility.

What is Sparksee?

Sparksee, formerly known as DEX, enhances both spatial efficiency and performance while keeping a streamlined design that allows for quick analysis of large-scale networks. It accommodates a variety of programming languages, such as .Net, C++, Python, Objective-C, and Java, which provides flexibility across numerous operating systems. The graph data is systematically organized using bitmap data structures, allowing for impressive compression ratios. These bitmaps are segmented into chunks that are aligned with disk pages, which improves input/output locality and leads to superior performance. By utilizing bitmaps, calculations are performed using binary logic instructions that support efficient processing in pipelined architectures. Additionally, the system includes comprehensive native indexing, ensuring swift access to all graph data structures. Connections between nodes are also represented as bitmaps, which further decreases their storage requirements. To optimize resource utilization, advanced I/O techniques are employed to limit the number of data pages loaded into memory. Each distinct value within the database is stored singularly, effectively reducing redundancy and enhancing overall efficiency. Consequently, this amalgamation of characteristics positions Sparksee as an exceptionally capable tool for conducting extensive graph data analyses, making it invaluable for researchers and data scientists alike.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

$200 per 6 moths
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

VelocityDB

Date Founded

2011

Company Location

United States

Company Website

velocitydb.com

Company Facts

Organization Name

Sparsity Technologies

Date Founded

2010

Company Location

Spain

Company Website

sparsity-technologies.com

Categories and Features

Categories and Features

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