Online or onsite, instructor-led live Apache Ignite training courses demonstrate through interactive hands-on practice the principles behind persistent and pure in-memory storage and how to create an in-memory computing project.
Apache Ignite training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Luxembourg onsite live Apache Ignite trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
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Recalling/reviewing keypoints of the topics discussed.
Paolo Angelo Gaton - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
Sufficient hands on, trainer is knowledgable
Chris Tan
Course: A Practical Introduction to Stream Processing
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Course: A Practical Introduction to Stream Processing
The lab exercises. Applying the theory from the first day in subsequent days.
Dell
Course: A Practical Introduction to Stream Processing
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Course: A Practical Introduction to Stream Processing
Ignite Course Outlines in Luxembourg
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in Luxembourg (online or onsite) is aimed at developers who wish to learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project.
By the end of this training, participants will be able to:
- Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.
- Achieve persistence without syncing data back to a relational database.
- Use Ignite to carry out SQL and distributed joins.
- Improve performance by moving data closer to the CPU, using RAM as a storage.
- Spread data sets across a cluster to achieve horizontal scalability.
- Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.
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