Greenplum Architecture and Data Modeling Training Course

Target Audience:

This course is designed for IT professionals seeking solutions for storing and processing large-scale datasets within a distributed system environment.

Course Objective:

To provide in-depth expertise in Apache Hadoop cluster administration.

This instructor-led live training in Slovakia (online or onsite) is intended for intermediate-level data scientists and engineers who aim to utilize Google Colab and Apache Spark for big data processing and analytics.

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

• Configure a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Big data analytics entails the examination of extensive and diverse datasets to identify correlations, hidden patterns, and actionable insights.

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

Through this instructor-led, live remote training, participants will acquire the skills necessary to conduct big data analytics in healthcare by engaging in a series of hands-on laboratory exercises.

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

• Install and configure big data analytics tools, including Hadoop MapReduce and Spark
• Grasp the distinct characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Explore big data systems and algorithms within the context of healthcare applications

Audience

• Developers
• Data Scientists

Course Format

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

Note

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

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. During this three-day course (with an optional fourth day), participants will gain insight into the business advantages and practical use cases of Hadoop and its ecosystem. They will learn how to plan for cluster deployment and future expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop environments. Additionally, attendees will practice bulk data loading, explore various Hadoop distributions, and hands-on manage Hadoop ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“The materials were exceptionally well-prepared and thoroughly covered. The laboratory sessions were extremely helpful and clearly organized.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

A blend 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 server clusters. This course provides developers with an introduction to key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands as one of the most widely adopted frameworks for processing Big Data across server clusters. This course explores data management within HDFS, alongside advanced applications of Pig, Hive, and HBase. These sophisticated programming techniques are designed to benefit experienced 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 understanding them is more accessible than it may seem.

This instructor-led live training in Slovakia (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 core 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 a storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions, such as Amazon S3, as well as 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 plan to build applications using HBase, as well as administrators responsible for managing HBase clusters.

The curriculum guides developers through HBase architecture, data modeling, and application development practices. It also covers integrating MapReduce with HBase and addresses key administration topics focused on performance optimization. The training is highly practical, featuring extensive lab exercises.

Duration: 3 days

Audience: Developers & Administrators

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

This instructor-led live training (available onsite or remotely) is designed for intermediate-level administrators and engineers who want to deploy, manage, secure, and optimize NiFi dataflows in production environments.

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

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

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

In this instructor-led, live training in Slovakia, participants will learn how to use Python and Spark together to analyze big data as they work on 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 Slovakia (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 is a data-centric platform that seamlessly integrates big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules empower organizations to conduct rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training (available online or on-site) targets intermediate-level data professionals who want to leverage the Rocket and Intelligence modules in Stratio effectively using PySpark. The focus is on mastering looping structures, user-defined functions, and implementing advanced data logic.

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

• Navigate and operate within the Stratio platform using its Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis tasks.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• For customized training requirements for this course, please contact us to arrange your schedule.