Programming with Big Data in R Training Course

Audience:

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

Goal:

To develop in-depth knowledge of Hadoop cluster administration.

Big data analytics is the process of examining vast volumes of diverse datasets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare sector generates massive amounts of complex, heterogeneous medical and clinical data. Applying big data analytics to this information holds immense potential for deriving insights that improve healthcare delivery. However, the sheer scale of these datasets presents significant challenges for analysis and practical implementation in clinical environments.

In this instructor-led, live remote training, participants will learn how to perform big data analytics in healthcare by progressing 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 handle medical data
• Study big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

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

Note

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

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This intensive three-day course (with an optional fourth day) equips attendees with a deep understanding of the business value and practical use cases of Hadoop and its surrounding ecosystem. Participants will learn how to plan for cluster deployment and scalability, as well as master the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop systems. The curriculum includes hands-on practice with bulk data loading, exploration of various Hadoop distributions, and the management of ecosystem tools. The course concludes with an in-depth discussion on securing clusters using Kerberos.

\u201c\u2026The materials were exceptionally well-prepared and thoroughly covered. The lab sessions were very helpful and meticulously organized.\u201d
\u2014 Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Professionals serving as Hadoop administrators.

Format

A blend of lectures and hands-on laboratories, maintaining an approximate split of 60% theoretical instruction and 40% practical labs.

Apache Hadoop stands out as the leading framework for processing Big Data across server clusters. This course serves as an introduction for developers to the key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands out as one of the most widely adopted frameworks for managing Big Data across server clusters. This course explores data management within HDFS, alongside advanced applications of Pig, Hive, and HBase. The sophisticated programming strategies covered here are particularly advantageous for seasoned Hadoop developers.

Audience: developers

Duration: three days

Format: lectures (50%) and hands-on labs (50%).

Target Audience

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

This instructor-led, live training in Italy (online or onsite) is designed for 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 key components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Utilize the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to serve as the storage engine for on-premise Spark deployments.
• Configure Spark to access alternative storage solutions, such as Amazon S3, and NoSQL database systems like Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform administrative tasks, including 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.

Participants will be guided through HBase architecture, data modeling, and application development. The curriculum also covers the integration of MapReduce with HBase and addresses key administration topics, with a focus on performance optimization. The course is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers and Administrators

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates the automated, real-time routing, transformation, and mediation of data between diverse systems, supported by a web-based interface that offers granular control.

This instructor-led live training, available both onsite and remotely, is tailored for intermediate-level administrators and engineers aiming to deploy, manage, secure, and optimize NiFi dataflows within production environments.

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

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

Format of the Course

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

Course Customization Options

• For information on arranging customized training for this course, please contact us.

In this instructor-led, live training in Italy, 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 course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by leveraging distributed computing principles.

In this instructor-led, live training in Italy, participants will learn how to combine Python and Spark to analyze big data while engaging in hands-on exercises.

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

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Italy (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.

Stratio serves as a unified, data-centric platform that seamlessly combines big data capabilities, artificial intelligence, and governance. Its Rocket and Intelligence modules empower organizations to rapidly explore, transform, and analyze data with advanced analytics tailored for enterprise needs.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to master the Rocket and Intelligence modules within Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and sophisticated data logic.

Upon completion, participants will gain the ability to:

• Effectively navigate and utilize the Rocket and Intelligence modules within the Stratio platform.
• Implement PySpark for efficient data ingestion, transformation, and analytical processes.
• Control data workflows and execute feature engineering tasks using loops and conditional logic.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Training Format

• Engaging interactive lectures and discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For tailored training requests, please contact us to make arrangements.

Target Audience

Course Format

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

This instructor-led, live training enables participants to master data manipulation and visualization techniques using the tools provided by Tidyverse.

Tidyverse comprises a suite of versatile R packages designed for cleaning, processing, modeling, and visualizing data. Key packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Individuals new to the R programming language
• Individuals new to data analysis and data visualization

• A mix of lectures, discussions, exercises, and extensive hands-on practice

• Conduct data analysis and generate compelling visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Convert processed data into informative charts, such as line plots, bar plots, and histograms
• Import and filter data from diverse sources, including Excel, CSV, and SPSS files