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What is Kraken?

Kraken is tailored to meet the needs of a wide range of users, from analysts to data scientists. This intuitive, no-code automated machine learning platform simplifies the intricate world of data science. By streamlining critical processes such as data preparation, cleaning, algorithm selection, model training, and deployment, Kraken makes these tasks accessible to individuals across various skill levels. Analysts and engineers will particularly appreciate how their existing data analysis skills allow them to quickly adapt to using Kraken. The platform features a user-friendly interface and integrated SONAR© training, which enables users to transition into citizen data scientists with ease. For experienced data scientists, Kraken provides advanced functionalities that boost both speed and workflow efficiency. Whether your work involves Excel, flat files, or requires on-the-fly analyses, the easy drag-and-drop CSV upload and Amazon S3 integration make building models a breeze. Furthermore, Kraken’s Data Connectors allow for smooth integration with your favorite data warehouses, business intelligence platforms, and cloud storage services, creating a holistic data science environment. With Kraken, users of all experience levels can effortlessly tap into the capabilities of machine learning, fostering a collaborative and innovative atmosphere for all.

What is Apache Mahout?

Apache Mahout is a powerful and flexible library designed for machine learning, focusing on data processing within distributed environments. It offers a wide variety of algorithms tailored for diverse applications, including classification, clustering, recommendation systems, and pattern mining. Built on the Apache Hadoop framework, Mahout effectively utilizes both MapReduce and Spark technologies to manage large datasets efficiently. This library acts as a distributed linear algebra framework and includes a mathematically expressive Scala DSL, which allows mathematicians, statisticians, and data scientists to develop custom algorithms rapidly. Although Apache Spark is primarily used as the default distributed back-end, Mahout also supports integration with various other distributed systems. Matrix operations are vital in many scientific and engineering disciplines, which include fields such as machine learning, computer vision, and data analytics. By leveraging the strengths of Hadoop and Spark, Apache Mahout is expertly optimized for large-scale data processing, positioning it as a key resource for contemporary data-driven applications. Additionally, its intuitive design and comprehensive documentation empower users to implement intricate algorithms with ease, fostering innovation in the realm of data science. Users consistently find that Mahout's features significantly enhance their ability to manipulate and analyze data effectively.

Media

Media

Integrations Supported

Amazon S3
Domo
Google Cloud BigQuery
Kryptos
Looker
MySQL
Pulsar Finance
Qlik Sense
SQL Server
SimpleSwap
Snowflake
Tableau
Tradoshi

Integrations Supported

Apache Spark
Hadoop

API Availability

API Availability

Has API

Pricing Information

$100 per month
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Big Squid

Date Founded

2009

Company Location

United States

Company Website

www.bigsquid.com/kraken-platform

Company Facts

Organization Name

Apache Software Foundation

Company Location

United States

Company Website

mahout.apache.org

Categories and Features

AI/ML Model Training

Not specified

Big Data

Data Cleansing
Data Visualization
Predictive Analytics

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Categories and Features

AI/ML Model Training

Not specified

Machine Learning

Not specified

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