Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Effortlessly extract, transform, and load (ETL) data for analytics and data science applications. Create seamless, code-free data flows directed towards data lakes and data marts. This functionality is included within Oracle’s extensive suite of integration tools. The user-friendly interface allows for easy configuration of integration parameters and automates the mapping of data between various sources and targets. You can utilize pre-built operators like joins, aggregates, or expressions to effectively manipulate your data. Central management of your processes enables the use of parameters to adjust specific configuration settings during runtime. Users can actively prepare their datasets and observe transformation results in real-time for process validation. Enhance your productivity and adjust data flows instantly, without needing to wait for execution completion. Additionally, this solution helps prevent broken integration flows and minimizes maintenance challenges as data schemas change over time, ensuring a smooth data management experience. This capability empowers users to focus on gaining insights from their data rather than grappling with technical difficulties.
Description
Machine learning reveals concealed patterns and valuable insights within enterprise data, ultimately adding significant value to businesses. Oracle Machine Learning streamlines the process of creating and deploying machine learning models for data scientists by minimizing data movement, incorporating AutoML technology, and facilitating easier deployment. Productivity for data scientists and developers is enhanced while the learning curve is shortened through the use of user-friendly Apache Zeppelin notebook technology based on open source. These notebooks accommodate SQL, PL/SQL, Python, and markdown interpreters tailored for Oracle Autonomous Database, enabling users to utilize their preferred programming languages when building models. Additionally, a no-code interface that leverages AutoML on Autonomous Database enhances accessibility for both data scientists and non-expert users, allowing them to harness powerful in-database algorithms for tasks like classification and regression. Furthermore, data scientists benefit from seamless model deployment through the integrated Oracle Machine Learning AutoML User Interface, ensuring a smoother transition from model development to application. This comprehensive approach not only boosts efficiency but also democratizes machine learning capabilities across the organization.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Oracle Cloud Infrastructure
Yes
Apache Hive
No
Apache Spark
No
Impala
No
Kinetica
No
MySQL
No
Oracle Database
No
PwC Check-In
No
Integrations
Oracle Cloud Infrastructure
Yes
Apache Hive
Yes
Apache Spark
Yes
Impala
Yes
Kinetica
Yes
MySQL
Yes
Oracle Database
Yes
PwC Check-In
Yes
Pricing Details
$0.04 per GB per hour
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Oracle
Founded
1977
Country
United States
Website
www.oracle.com/integration/oracle-cloud-infrastructure-data-integration/
Vendor Details
Company Name
Oracle
Founded
1977
Country
United States
Website
www.oracle.com/data-science/machine-learning/
Product Features
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
No
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No
Integration
Dashboard
No
ETL - Extract / Transform / Load
No
Metadata Management
No
Multiple Data Sources
No
Web Services
No
Product Features
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No