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What is SplineCloud?

SplineCloud is a collaborative platform designed for knowledge management, focusing on improving the recognition, formalization, and distribution of structured and reusable knowledge specifically in science and engineering domains. This cutting-edge platform facilitates users in systematically organizing their information within structured repositories, making it both easily discoverable and accessible. Within its array of features, users can utilize an online plot digitizer that assists in extracting data from graphical formats, as well as an interactive curve fitting tool that allows for the establishment of functional relationships among datasets through the use of smooth spline functions. Users are also empowered to integrate datasets and relationships into their models and calculations by accessing them directly through the SplineCloud API or utilizing open-source client libraries that work seamlessly with Python and MATLAB. By fostering the development of reusable engineering and analytical applications, SplineCloud aims to reduce redundancies in the design process, protect valuable expert knowledge, and boost efficiency in decision-making. Furthermore, the platform stands as an indispensable asset for researchers and engineers who are striving to refine their workflows and enhance the exchange of knowledge within their respective fields, thereby contributing to a more collaborative and innovative environment.

What is Dremio?

Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.

Media

Media

Integrations Supported

Integrations Supported

AccessOwl
Apache Iceberg
Apache Superset
Azure Marketplace
BI Book
Cyral
DashboardFox
DataClarity Unlimited Analytics
Emgage
HPE Ezmeral
Looker
Microsoft Power Query
Okera
Privacera
Protegrity
PuppyGraph
SQL
Tableau
Yurbi
data.world

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

Company Facts

Organization Name

SplineCloud

Date Founded

2021

Company Location

Ukraine

Company Website

splinecloud.com

Company Facts

Organization Name

Dremio

Date Founded

2015

Company Location

United States

Company Website

www.dremio.com

Categories and Features

Data Engineering

Not specified

Categories and Features

Big Data

Not specified

Data Engineering

Not specified

Data Lake

Not specified

Data Lineage

Not specified

Data Virtualization

Not specified

Data Warehouse

Not specified

Query Engines

Not specified

Semantic Layer

Not specified

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