Online or onsite, instructor-led live Apache Hadoop training courses demonstrate through interactive hands-on practice the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems.
Hadoop 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. Onsite live Hadoop training can be carried out locally on customer premises in Helsinki or in NobleProg corporate training centers in Helsinki.
This instructor-led, live training in Helsinki (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.
By the end of this training, participants will be able to:
Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
Understand the features, core components, and architecture of Spark and Hadoop.
Learn how to integrate Spark, Hadoop, and Python for big data processing.
Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
Use Apache Mahout to scale machine learning algorithms.
This instructor-led live training in Helsinki is designed for intermediate administrators aiming to deploy and manage Apache NiFi in production. Participants will learn to configure clusters, design dataflows, and optimize performance through hands-on labs and scenario-based exercises.
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Testimonials (2)
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
James - BHG Financial
Course - Apache NiFi for Administrators
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
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