Online Hadoop Training in Norway

This instructor-led, live training in Norway (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.

Audience:

This course is designed for IT professionals who are seeking a solution to store and process large data sets in a distributed system environment.

Goal:

To gain comprehensive knowledge in Hadoop cluster administration.

Big data analytics involves the process of examining large and varied data sets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare industry is inundated with vast amounts of complex and diverse medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that can improve healthcare delivery. However, the sheer volume and complexity of these datasets present substantial challenges in analysis and practical application within a clinical setting.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in healthcare 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 manage and analyze medical data
• Study big data systems and algorithms in the context of healthcare applications

Audience

• Developers
• Data Scientists

Format of the Course

• Part lecture, part discussion, with exercises and extensive hands-on practice.

Note

• To request a customized training for this course, please contact us to arrange.

Apache Hadoop is the leading framework for processing Big Data on clusters of servers. In this three-day (or optionally four-day) course, participants will gain insights into the business benefits and use cases of Hadoop and its ecosystem. They will learn how to plan cluster deployment and expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop. The course also includes practical sessions on bulk data loading, an overview of various Hadoop distributions, and hands-on experience with installing and managing Hadoop ecosystem tools. The training concludes with a discussion on securing the cluster using 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

A combination of lectures and hands-on labs, with an approximate balance of 60% lectures and 40% labs.

Apache Hadoop is the leading framework for processing Big Data across clusters of servers. In this course, developers will be introduced to various components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop is one of the most widely used frameworks for processing Big Data on clusters of servers. This course explores data management in HDFS, advanced Pig, Hive, and HBase. These sophisticated programming techniques will be particularly useful for experienced Hadoop developers.

Audience: developers

Duration: three days

Format: lectures (50%) and practical labs (50%).

Audience:

This course is designed to clarify the concepts of big data and Hadoop technology, demonstrating that they are not as complex as they might seem.

This instructor-led, live training in Norway (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 provides an introduction to HBase, a NoSQL database built on top of Hadoop. It is designed for developers who will be using HBase to create applications and administrators who will manage HBase clusters.

The course takes developers through the architecture of HBase, data modeling, and application development. It also covers the integration of MapReduce with HBase and discusses various administration topics related to performance optimization. The course is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between different systems, offering a web-based user interface and detailed control.

This instructor-led, live training (either onsite or remote) is targeted at intermediate-level administrators and engineers who aim 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 a variety of sources and destinations.
• Implement automation, routing, and transformation logic for data flows.
• Optimize performance, monitor operations, and resolve issues effectively.

Format of the Course

• Interactive lecture with discussions on real-world architecture.
• Hands-on labs: building, deploying, and managing data 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 Norway, 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.