Data Streaming and Real Time Data Processing Training Course

Audience:

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

Goal:

To provide deep expertise in administering Hadoop clusters.

This instructor-led live training in Sweden (online or onsite) targets intermediate-level data scientists and engineers who intend 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 entails the examination of extensive and diverse datasets to identify correlations, hidden patterns, and other valuable insights.

The healthcare sector manages vast volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics on health records 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 within clinical settings.

In this instructor-led, remote live training, participants will learn how to conduct big data analytics in healthcare by progressing through a series of practical, live laboratory exercises.

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

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

Audience

• Developers
• Data Scientists

Format of the Course

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

Note

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

Apache Hadoop is 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 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, familiarize themselves with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The training 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 system administrators

Format

A blend of lectures and hands-on labs, with an approximate distribution 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 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 explores data management within HDFS, alongside advanced techniques using Pig, Hive, and HBase. These sophisticated programming strategies are designed to support experienced Hadoop developers in enhancing their skill set.

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 comprehend.

This instructor-led, live training in Sweden (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 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 curriculum guides developers through HBase architecture, data modeling, and application development. It also covers the integration of MapReduce with HBase and addresses key administrative topics related to performance optimization. The course is highly practical, featuring numerous laboratory 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 Sweden, 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 constructing scalable data processing and Machine Learning workflows with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process substantial datasets efficiently by leveraging distributed computing principles.

In this instructor-led live training, participants will learn how to utilize Python and Spark together to analyze big data through 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 Sweden (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 unified platform centered on data that combines big data capabilities, artificial intelligence, and governance into one cohesive solution. The Rocket and Intelligence modules within Stratio facilitate swift data exploration, transformation, and sophisticated analytics in enterprise settings.

This instructor-led live training (available online or onsite) is designed for intermediate-level data professionals looking to leverage the Rocket and Intelligence modules in Stratio effectively with PySpark. The curriculum emphasizes looping structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

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