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What is Apache TomEE?

Apache TomEE, commonly known as "Tommy," is an application server that boasts full certification for Jakarta EE 9.1 and is built on the foundation of Apache Tomcat, starting from a standard Apache Tomcat zip file. The creation of TomEE begins with Apache Tomcat, and through the integration of essential libraries, it evolves into a packaged version that encompasses EE capabilities, resulting in the TomEE server. This server is recognized for its stability and readiness for production environments, as Apache TomEE 8.0 implements Java EE 8/Jakarta EE 8 while still supporting the javax namespace and functioning on Java 8 or later versions. It aligns primarily with the Jakarta EE 9.1 web profile and adopts the new jakarta namespace, ensuring compatibility with Java 11 or newer. There are four distinct variations of Apache TomEE available: web profile, MicroProfile, Plus, and Plume. The web profile variant of Apache TomEE delivers fundamental services such as servlets, JSP, JSF, JTA, JPA, CDI, bean validation, and EJB Lite, forming the core of the server's functionality. Meanwhile, the MicroProfile edition enriches the server with extended support for MicroProfile features, while the Plus and Plume versions further broaden capabilities by incorporating JMS, JAX-WS, and additional functionalities. In summary, Apache TomEE serves as a flexible and powerful option for developers who aim to utilize Jakarta EE in their software projects, making it an ideal choice for modern application development.

What is Amazon EMR?

Amazon EMR is recognized as a top-tier cloud-based big data platform that efficiently manages vast datasets by utilizing a range of open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. This innovative platform allows users to perform Petabyte-scale analytics at a fraction of the cost associated with traditional on-premises solutions, delivering outcomes that can be over three times faster than standard Apache Spark tasks. For short-term projects, it offers the convenience of quickly starting and stopping clusters, ensuring you only pay for the time you actually use. In addition, for longer-term workloads, EMR supports the creation of highly available clusters that can automatically scale to meet changing demands. Moreover, if you already have established open-source tools like Apache Spark and Apache Hive, you can implement EMR on AWS Outposts to ensure seamless integration. Users also have access to various open-source machine learning frameworks, including Apache Spark MLlib, TensorFlow, and Apache MXNet, catering to their data analysis requirements. The platform's capabilities are further enhanced by seamless integration with Amazon SageMaker Studio, which facilitates comprehensive model training, analysis, and reporting. Consequently, Amazon EMR emerges as a flexible and economically viable choice for executing large-scale data operations in the cloud, making it an ideal option for organizations looking to optimize their data management strategies.

Media

Media

Integrations Supported

Apache Tomcat
Maven

Integrations Supported

AWS App Mesh
AWS Lake Formation
AWS Step Functions
Amazon SageMaker Unified Studio
Apache Hive
Apache Phoenix
Ataccama ONE
Data Virtuality
Immuta
Okera
Pelanor
Pepperdata
Prophecy
Protegrity
Quorso
SAS Studio
Saagie

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

Windows
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Apache

Company Location

United States

Company Website

tomee.apache.org

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/emr/

Categories and Features

Application Server

Not specified

Categories and Features

Big Data

Not specified

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