Hadoop Training in Abu Dhabi

This instructor-led, live training in Abu Dhabi (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 seeking solutions to manage and analyze extensive data sets within a distributed system framework.

Goal:

To gain comprehensive expertise in the administration of Hadoop clusters.

The analysis of big data entails examining extensive and diverse datasets to discover correlations, concealed patterns, and other valuable insights.

The healthcare sector possesses vast quantities of intricate and varied medical and clinical information. Leveraging big data analytics on health-related data offers significant potential for enhancing the delivery of healthcare through derived insights. However, the sheer volume of these datasets presents substantial challenges in both analysis and practical implementation within a clinical setting.

In this instructor-led live training (conducted remotely), participants will learn how to conduct big data analytics in healthcare by working through a series of hands-on lab exercises.

Upon completion of this training, participants will be able to:

• Set up and configure big data analytics tools such as Hadoop MapReduce and Spark
• Comprehend the attributes of medical data
• Utilize big data methodologies for handling medical information
• Examine big data systems and algorithms within healthcare applications

Audience

• Software Developers
• Data Scientists

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Note

• To arrange a customized training for this course, please contact us to make the necessary arrangements.

Apache Hadoop is the leading framework for processing Big Data across clusters of servers. This course, which spans three days (or optionally four), will equip participants with knowledge on the business advantages and practical applications of Hadoop and its ecosystem. Attendees will learn how to plan cluster deployment and expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop systems. They will also gain hands-on experience in loading bulk data into clusters, become acquainted with different Hadoop distributions, and practice setting up and managing tools within the Hadoop ecosystem. The course concludes with a discussion on securing clusters using Kerberos.

“…The materials were meticulously prepared and covered comprehensively. The Lab was very beneficial and well-organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

The course includes lectures and hands-on labs, with an approximate 60% lecture to 40% lab balance.

Apache Hadoop stands as the leading framework for handling Big Data across server clusters. This course will familiarize developers with the different elements of the Hadoop environment, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands out as a leading framework for managing Big Data across server clusters. This course explores data handling in HDFS, along with advanced techniques in Pig, Hive, and HBase. These sophisticated programming methods will be particularly advantageous for seasoned Hadoop developers.

Audience: Developers

Duration: Three days

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

Audience:

This course aims to simplify big data and Hadoop technology, demonstrating that understanding these concepts is within reach.

This instructor-led, live training in Abu Dhabi (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 utilizing HBase in application development and administrators responsible for managing HBase clusters.

Participants will gain insight into the architecture and data modeling aspects of HBase, as well as application development practices. The course also covers integrating MapReduce with HBase and explores various administration topics focused on performance optimization. Practical experience is emphasized through numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

Informatica with Big Data (BDM) is a course designed to empower data professionals to develop, manage and analyze large data sets, leveraging the latest technologies and architectures in the Big Data space. The course focuses on the full life cycle from data ingestion, integration, cleansing and curation, to data analytics and the exposure and consumption of big data services.
In this course, learners will explore the solutions that process large datasets using Big Data technologies and architectures like Apache Hive, Apache Hadoop, and Apache Spark. The course also provides practical experience in using Informatica tools like Bloombox, Big Data Management, and iData Fabric to understand big data technologies like Map Reduce and Hadoop. By the end of this course, the learner will be able to create end-to-end data solutions using Informatica and its associated Big Data solutions.

Apache NiFi (Hortonworks DataFlow) is a real-time data logistics and simple event processing platform that facilitates the movement, tracking, and automation of data across different systems. It uses flow-based programming and offers a web-based user interface for managing dataflows in real time.

In this instructor-led training session, which can be conducted either on-site or remotely, participants will learn to deploy and manage Apache NiFi within a live lab environment.

By the end of the training, participants will be able to:

• Install and configure Apache NiFi.
• Source, transform, and manage data from various distributed sources such as databases and big data lakes.
• Automate dataflows.
• Enable streaming analytics.
• Implement different methods for data ingestion.
• Transform Big Data into actionable business insights.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live lab environment.

Course Customization Options

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

In this instructor-led, live training in Abu Dhabi, 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.