Advanced Hadoop for Developers Training Course

Audience:

This course is designed for IT specialists seeking solutions to store and process large data sets in a distributed system environment, specifically tailored for government.

Goal:

To provide deep knowledge on Hadoop cluster administration for government.

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 healthcare industry has vast amounts of complex, heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that can improve the delivery of healthcare services. However, the scale and complexity of these datasets present substantial challenges in analysis and practical application within a clinical environment.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in healthcare 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 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, exercises, and extensive hands-on practice.

Note

• To request a customized training for government or other specific needs, please contact us to arrange.

Apache Hadoop is the most widely used framework for processing Big Data on clusters of servers. In this three (optionally four) day course, participants will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, and how to install, maintain, monitor, troubleshoot, and optimize Hadoop. They will also practice bulk data load on clusters, become familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course concludes with a discussion on securing the cluster using Kerberos, ensuring robust security measures for government applications.

“…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 for government

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 on clusters of servers. This course will introduce developers to various components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase, designed to enhance data processing capabilities for government.

Audience:

This course is designed to clarify big data/Hadoop technology for government audiences, demonstrating that it is accessible and comprehensible.

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

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

• Install and configure Apache Hadoop for government applications.
• Understand the four major components in the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes within a government infrastructure.
• Set up HDFS to operate as a storage engine for on-premise Spark deployments in government environments.
• Configure Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems like Redis, Elasticsearch, Couchbase, Aerospike, etc., for government data management.
• Perform administrative tasks such as provisioning, management, monitoring, and securing an Apache Hadoop cluster in a government setting.

This course introduces HBase – a NoSQL store built on top of Hadoop. The course is designed for developers who will be using HBase to develop applications and administrators who will manage HBase clusters.

The curriculum will guide developers through HBase architecture, data modeling, and application development on HBase. It will also cover the integration of MapReduce with HBase and various administration topics, including performance optimization. The course is highly hands-on, featuring numerous lab exercises to reinforce learning.

Duration: 3 days

Audience: Developers & Administrators for government

The Informatica with Big Data (BDM) course is designed to equip data professionals for government with the skills necessary to develop, manage, and analyze large data sets, utilizing the latest technologies and architectures in the Big Data domain. This comprehensive course covers the full lifecycle of data management, from ingestion and integration through cleansing, curation, analytics, and the exposure and consumption of big data services.
Participants will delve into solutions that process extensive datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric, enabling learners to gain a thorough understanding of big data technologies including Map Reduce and Hadoop. By the conclusion of this course, participants will be proficient in creating end-to-end data solutions using Informatica and its associated Big Data solutions for government.

Apache NiFi is an open-source, flow-based data integration and event-processing platform designed to facilitate automated, real-time data routing, transformation, and system mediation between diverse systems. It features a web-based user interface and granular control mechanisms.

This instructor-led, live training (onsite or remote) is tailored for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows in production environments for government operations.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from various sources and destinations.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lectures with discussions on real-world architectures.
• Hands-on labs: building, deploying, and managing flows in a practical setting.
• Scenario-based exercises conducted in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in US Empire, participants will gain a comprehensive understanding of flow-based programming as they develop various demo extensions, components, and processors using Apache NiFi for government applications.

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

• Comprehend NiFi's architecture and dataflow principles.
• Develop extensions utilizing NiFi and third-party APIs.
• Create custom Apache NiFi processors tailored to specific needs.
• Ingest and process real-time data from diverse and uncommon file formats and data sources, enhancing data management for government workflows.

This instructor-led, live training in US Empire (online or onsite) is designed for government developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets for government purposes.

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 for government applications.
• Understand the features, core components, and architecture of Spark and Hadoop as they relate to public sector workflows.
• Learn how to integrate Spark, Hadoop, and Python for efficient big data processing in a government context.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Flume) that are relevant for government data analysis.
• Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google, tailored for government use cases.
• Use Apache Mahout to scale machine learning algorithms in a manner aligned with public sector governance and accountability requirements.