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 The Hague or in NobleProg corporate training centers in The Hague.
NobleProg -- Your Local Training Provider
The Hague
The Office Operators , Schenkkade 50, The Hague, Netherlands, 2595 AR
Attend training courses at a top location in the business district of The Hague with Professional and modern meeting rooms fitted to your needs, with excellent accessibility. The Haagsche Zwaan has it all.
In the center just a few minutes away from the train station Den Haag Laan van NOI and the exit 3 of highway A12 you will find De Haagse Zwaan. Train and metro (line E The Hague Central or Laan NOI) are at 8 minutes walking distance. Tram stop Beatrixkwartier for the Randstadrail line 3 and 4 is at a 3 minute walk.
This instructor-led, live training in The Hague (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.
Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.
The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.
In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises.
By the end of this training, participants will be able to:
Install and configure big data analytics tools such as Hadoop MapReduce and Spark
Understand the characteristics of medical data
Apply big data techniques to deal with medical data
Study big data systems and algorithms in the context of health applications
Audience
Developers
Data Scientists
Format of the Course
Part lecture, part discussion, exercises and heavy hands-on practice.
Note
To request a customized training for this course, please contact us to arrange.
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising
Audience
Hadoop administrators
Format
Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.
Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.
This instructor-led, live training in The Hague (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.
By the end of this training, participants will be able to:
Install and configure Apache Hadoop.
Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
Set up HDFS to operate as storage engine for on-premise Spark deployments.
Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
This course introduces HBase – a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications, and administrators who will manage HBase clusters.
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.
This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.
By the end of this training, participants will be able to:
Install, configure, and maintain Apache NiFi clusters.
Design and manage dataflows from varied sources and sinks.
Implement flow automation, routing, and transformation logic.
Optimize performance, monitor operations, and troubleshoot issues.
Format of the Course
Interactive lecture with real-world architecture discussion.
Hands-on labs: building, deploying, and managing flows.
Scenario-based exercises in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training in The Hague, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.
By the end of this training, participants will be able to:
Understand NiFi's architecture and dataflow concepts.
Develop extensions using NiFi and third-party APIs.
Custom develop their own Apache Nifi processor.
Ingest and process real-time data from disparate and uncommon file formats and data sources.
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Testimonials (3)
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
instructor was clear and explained thing well. The course and excercises were easy to follow along with.
Peter - Army
Course - Apache NiFi for Developers
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.
Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data
Provisional Upcoming Courses (Contact Us For More Information)
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