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Red Hat JBoss Data Grid Development (JB453VT)

Learn how to install a data grid, develop a cache-based application, cluster and secure caches, and more

Virtual

Learning Style

Red Hat

Provider

Intermediate

Difficulty

5 Days

Course Duration

Course Info

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Learn how to install a data grid, develop a cache-based application, cluster and secure caches, and more

Course Information

About this course:

Learn how to install a data grid, develop a cache-based application, cluster and secure caches, and more

Red Hat® JBoss® Data Grid Development (JB453) focuses on the configuration of caches and manipulation of cache entries. Students will learn how to access caches in library and remote modes (clustered and non-clustered). In addition, students will learn how to secure data grids, encrypt data grid communications, and search the data grid.

Course Objective:

  • Understand data grids and Red Hat JBoss Data Grid
  • Install Red Hat JBoss Data Grid
  • Develop a cache-based application
  • Access a remote cache
  • Cluster caches
  • Persist the cache
  • Secure the cache
  • Search the data grid

Audience:

  • Java developers
  • DevOps professionals who will administer Red Hat JBoss Data Grid

Prerequisite:

  • Java programming skills
  • Red Hat JBoss Developer Studio or Eclipse experience

Outline

More Information

More Information
Brand Red Hat
Subjects Big Data
Lab Access No
Technology Red Hat
Learning Style Virtual Classroom
Learning Type Course
Difficulty Intermediate
Course Duration 5 Days
Language English

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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