Data Streaming and Real Time Data Processing Training Course

Target Audience:

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

Objective:

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

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

By the end 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 is the process of examining vast, diverse datasets to uncover correlations, hidden patterns, and actionable insights.

The healthcare industry generates enormous volumes of complex, heterogeneous medical and clinical data. Applying big data analytics to this information holds significant potential for deriving insights that improve healthcare delivery. However, 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 healthcare by working through a series of hands-on, live laboratory exercises.

By the conclusion 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 within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• A blend of lectures, discussions, 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. Over the course of three days (with an optional fourth day), participants will explore the business advantages and practical use cases of Hadoop and its associated ecosystem. They will gain expertise in planning cluster deployment and scalability, as well as installing, maintaining, monitoring, troubleshooting, and optimizing Hadoop environments. Additional hands-on activities include performing bulk data loads on clusters, becoming familiar with various Hadoop distributions, and managing Hadoop ecosystem tools. The course concludes with an in-depth discussion on securing the cluster using Kerberos.

\u201c\u2026The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized\u201d
\u2014 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 most widely used framework for processing Big Data across server clusters. This course introduces developers to the 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 provides an in-depth exploration of data management within HDFS, alongside advanced usage of Pig, Hive, and HBase. These advanced programming strategies are particularly valuable for experienced Hadoop developers looking to expand their expertise.

Audience: Developers

Duration: Three days

Format: The curriculum is divided equally between lectures (50%) and hands-on lab exercises (50%).

Target Audience:

This course aims to demystify big data and Hadoop technologies, demonstrating that they are accessible and not overly complex to comprehend.

This instructor-led live training in Romania (online or onsite) is tailored for system administrators seeking to learn how to set up, deploy, and manage Hadoop clusters within their organizations.

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

• Install and configure Apache Hadoop.
• Understand the four major components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to function as a storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions like Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform 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.

The program guides developers through HBase architecture, data modeling, and application development practices. It also covers integrating MapReduce with HBase and addresses key administration topics, particularly those focused on performance optimization. The course is highly practical, featuring numerous lab exercises to reinforce learning.

Duration : 3 days

Audience : Developers & Administrators

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based UI and fine-grained control.

This instructor-led, live training (available onsite or remote) is designed for 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 Romania, 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 developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

In this instructor-led, live training in Romania, 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 Romania (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 combines big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training session, available both online and on-site, is designed for data professionals at an intermediate level who aim to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum focuses on looping structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be capable of:

• Navigating and operating within the Stratio platform using the Rocket and Intelligence modules.
• Applying PySpark for data ingestion, transformation, and analysis.
• Utilizing loops and conditional logic to manage data workflows and feature engineering tasks.
• Creating and managing user-defined functions (UDFs) to enable reusable data operations in PySpark.

Format of the Course

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

Course Customization Options

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