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Description
Apache TomEE, affectionately known as “Tommy”, is a certified application server for Jakarta EE 9.1, built upon the foundation of Apache Tomcat by utilizing a standard Apache Tomcat zip file. The process begins with the base Apache Tomcat, to which we integrate our specific libraries and then package everything together. The end product is essentially Tomcat enhanced with additional EE features, resulting in TomEE. This server is stable and production-ready, with Apache TomEE 8.0 implementing Java EE 8/Jakarta EE 8 while maintaining support for the javax namespace, and it operates on Java 8 or later versions. Furthermore, it aligns closely with the Jakarta EE 9.1 web profile and embraces the new jakarta namespace, requiring Java 11 or more advanced versions. Apache TomEE is available in four distinct variations: web profile, MicroProfile, Plus, and Plume, each tailored for specific requirements. The web profile of Apache TomEE includes essential components such as servlets, JSP, JSF, JTA, JPA, CDI, bean validation, and EJB Lite. Meanwhile, Apache TomEE MicroProfile introduces functionalities that cater to MicroProfile needs, while TomEE Plus and Plume extend capabilities to include JMS, JAX-WS, and several other features. With its robust architecture and diverse profiles, Apache TomEE is designed to accommodate a wide array of enterprise applications.
Description
MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Java
Yes
Amazon EC2
No
Apache Cassandra
No
Apache Geronimo
Yes
Apache HBase
No
Apache Hive
No
Apache Mesos
No
Apache Spark
No
Apache Tomcat
Yes
Hadoop
No
Integrations
Java
Yes
Amazon EC2
Yes
Apache Cassandra
Yes
Apache Geronimo
No
Apache HBase
Yes
Apache Hive
Yes
Apache Mesos
Yes
Apache Spark
Yes
Apache Tomcat
No
Hadoop
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
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
No
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)
No
In Person
Yes
Vendor Details
Company Name
Apache
Country
United States
Website
tomee.apache.org
Vendor Details
Company Name
Apache Software Foundation
Founded
1995
Country
United States
Website
spark.apache.org/mllib/
Product Features
Application Server
Admin Console
No
Alerts / Notifications
No
Application Security
No
Multi-Application Support
No
Multiple Environment Support
No
Open Standards Compliance
No
Reporting / Analytics
No
User Management
No
Product Features
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
