Advanced Hadoop for Developers Training Course

Audience:

This course is designed for IT professionals seeking strategies to store and process large datasets within a distributed system environment.

Goal:

To develop in-depth expertise in administering Hadoop clusters.

Big data analytics entails the examination of vast and diverse datasets to identify correlations, uncover hidden patterns, and extract valuable insights.

The healthcare sector generates enormous volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics within this domain holds significant potential for generating insights that enhance healthcare delivery. Nevertheless, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation in clinical settings.

In this instructor-led, live remote training, participants will learn how to conduct big data analytics in health through a series of hands-on live laboratory 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 key characteristics of medical data
• Apply big data techniques to manage medical data
• Study big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• A blend of lectures, discussions, exercises, and intensive hands-on practice.

Note

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

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. Over the course of three days (with a fourth day available), participants will explore the business advantages and practical use cases of Hadoop and its ecosystem. The curriculum covers planning cluster deployment and scalability, as well as installing, maintaining, monitoring, troubleshooting, and optimising Hadoop. Attendees will also gain hands-on experience with bulk data loading on clusters, become familiar with various Hadoop distributions, and practice installing and managing tools within the Hadoop ecosystem. The course concludes with a discussion on securing clusters 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

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. This course introduces developers to the key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Target Audience:

This course aims to demystify big data and Hadoop technologies, demonstrating that they are accessible and straightforward to comprehend.

This instructor-led live training in Portugal (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organisation.

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 intend to build applications using HBase, as well as administrators responsible for managing HBase clusters.

We will guide developers through HBase architecture, data modelling, and application development on the platform. The course also covers the integration of MapReduce with HBase and addresses key administration topics related to performance optimization. This is a highly practical course featuring numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

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 Portugal, 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.

This instructor-led, live training in Portugal (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.