Data Streaming and Real Time Data Processing Training Course

Target Audience:

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

Learning Objectives:

Gain in-depth knowledge of Hadoop cluster administration.

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

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

• Set up 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 refers to the process of inspecting vast amounts of diverse data sets to identify correlations, uncover hidden patterns, and generate valuable insights.

The healthcare sector generates enormous volumes of complex, heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that enhance healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation within clinical environments.

Through this instructor-led, live remote training, participants will learn how to conduct big data analytics in the health sector by completing a series of hands-on live laboratory exercises.

Upon completion 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 medical data
• Study big data systems and algorithms in the context of health applications

Audience

• Developers
• Data Scientists

Course Format

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

Note

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

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 explore the business advantages and practical applications of Hadoop and its surrounding ecosystem. The curriculum covers cluster deployment planning, expansion strategies, and the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop. Attendees will gain hands-on experience with bulk data loading on clusters, become familiar with various Hadoop distributions, and practice installing and managing ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“…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

Format

The course combines lectures with hands-on labs, maintaining an approximate balance of 60% lectures and 40% labs.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course provides developers with an introduction 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 managing Big Data across server clusters. This course offers an in-depth exploration of data management within HDFS, alongside advanced applications of Pig, Hive, and HBase. These sophisticated programming techniques are designed to provide significant value to experienced Hadoop developers.

Audience: developers

Duration: three days

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

Target Audience:

This course aims to simplify big data and Hadoop technology, demonstrating that it is accessible and not overly complex to learn.

This instructor-led live training in Norway (online or onsite) is designed for system administrators who wish 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 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 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. It also covers the integration of MapReduce with HBase and addresses key administration topics related to performance optimization. The training is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

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

This instructor-led, live training (onsite or remote) is aimed at 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 Norway, 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 constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into 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 Norway, participants will learn how to utilize Python and Spark together to analyze big data while working on hands-on exercises.

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

• Master the use of Spark with Python to analyze Big Data.
• Tackle exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Norway (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 integrates big data, AI, and governance into a single solution. Its Rocket and Intelligence modules enable rapid data exploration, transformation, and advanced analytics in enterprise environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

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

• Navigate and work within the Stratio platform using Rocket and Intelligence modules.
• Apply PySpark in the context of data ingestion, transformation, and analysis.
• Use loops and conditional logic to control data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

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

Course Customization Options

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