Medellín - San Fernando Plaza

06th Floor Torre Proteccion, Medellín, colombia
The center occupies the sixth floor of the complex. As well as being steps away from an excellent food court and other amenit...

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Apache Apex: Processing Big Data-in-Motion

21 Hours
Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable. This instructor-led, live training introduces Apache Apex's unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop. By the end of this training, participants will be able to:
  • Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc.
  • Build, scale and optimize an Apex application
  • Process real-time data streams reliably and with minimum latency
  • Use Apex Core and the Apex Malhar library to enable rapid application development
  • Use the Apex API to write and re-use existing Java code
  • Integrate Apex into other applications as a processing engine
  • Tune, test and scale Apex applications
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Testimonials (3)

Sufficient hands on, trainer is knowledgable

Chris Tan
Course - A Practical Introduction to Stream Processing

Las explicaciones eran muy buenas, si bien algunas preguntas pudieron ahorrarse si se hubieran tocado esos puntos al inicio de los temas se notó un buen dominio y experiencia en el tema.

Alan Jaime Rodríguez García - BANCO DE MEXICO
Course - Stream Processing with Kafka Streams

Recalling/reviewing keypoints of the topics discussed.

Paolo Angelo Gaton - SMS Global Technologies Inc.
Course - Building Stream Processing Applications with Kafka Streams

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