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What is dotData?

dotData enables your organization to focus on the results of artificial intelligence and machine learning projects, simplifying the intricate data science process by automating the entire lifecycle of data science. With the ability to initiate a comprehensive AI and ML pipeline within minutes, you gain the advantage of real-time updates through continuous deployment. This groundbreaking approach expedites data science projects, cutting down timelines from months to just days thanks to automated feature engineering. By leveraging data science automation, discovering valuable insights hidden within your business becomes a hassle-free task. Traditionally, engaging with data science to build and implement accurate machine learning and AI models is often a labor-intensive and protracted process, requiring collaboration among various specialists. By automating the most monotonous and repetitive aspects of data science, you can drastically reduce the time needed for AI development, shrinking it from several months to just a few days. This transformation not only improves overall efficiency but also empowers teams to concentrate on more strategic and innovative initiatives, ultimately driving better business outcomes. Such advancements in automation are reshaping the landscape of data science, making it more accessible and effective for organizations of all sizes.

What is IBM Analytics for Apache Spark?

IBM Analytics for Apache Spark presents a flexible and integrated Spark service that empowers data scientists to address ambitious and intricate questions while speeding up the realization of business objectives. This accessible, always-on managed service eliminates the need for long-term commitments or associated risks, making immediate exploration possible. Experience the benefits of Apache Spark without the concerns of vendor lock-in, backed by IBM's commitment to open-source solutions and vast enterprise expertise. With integrated Notebooks acting as a bridge, the coding and analytical process becomes streamlined, allowing you to concentrate more on achieving results and encouraging innovation. Furthermore, this managed Apache Spark service simplifies access to advanced machine learning libraries, mitigating the difficulties, time constraints, and risks that often come with independently overseeing a Spark cluster. Consequently, teams can focus on their analytical targets and significantly boost their productivity, ultimately driving better decision-making and strategic growth.

Media

Media

Integrations Supported

Apache Spark
Microsoft Azure
RadiantOne
Switch Automation

Integrations Supported

Apache Spark
Microsoft Azure
RadiantOne
Switch Automation

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
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

dotData

Date Founded

2018

Company Location

United States

Company Website

dotdata.com

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/analytics/ca/en/technology/cloud-data-services/spark-as-a-service/

Categories and Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Categories and Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Integration

Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services

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