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 develop in-depth expertise in administering Hadoop clusters.

This instructor-led live training in Czech Republic (online or onsite) targets intermediate-level data scientists and engineers who wish to employ 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 encompasses the methodology of reviewing extensive and diverse datasets to identify correlations, uncover latent patterns, and extract actionable insights.

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

In this instructor-led, live online training, participants will learn how to execute big data analytics in healthcare by progressing through 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
• Comprehend the characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Examine big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• A combination 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 is the leading framework for processing Big Data across server clusters. This course, lasting three days (with an optional fourth day), covers the business advantages and practical use cases of Hadoop and its surrounding ecosystem. Attendees will learn how to plan cluster deployment and expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop environments. Practical exercises include bulk data loading into clusters, exploring various Hadoop distributions, and installing and managing tools within the Hadoop ecosystem. The curriculum 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

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 clusters of servers. This course introduces developers to the key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop is one of the most widely used frameworks for processing Big Data across server clusters. This course explores data management in HDFS, as well as advanced techniques using Pig, Hive, and HBase. These sophisticated programming skills are particularly valuable for experienced Hadoop developers.

Audience: developers

Duration: three days

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

Audience:

This course aims to demystify big data and Hadoop technology, demonstrating that it is accessible and straightforward to understand.

This instructor-led, live training in Czech Republic (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 datastore 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.

We will guide developers through HBase architecture, data modeling, and application development. The curriculum also covers the integration of MapReduce with HBase and addresses key administration topics, focusing on performance optimization. The training is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

Apache NiFi is an open-source platform for data integration and event processing that operates on a flow-based model. It facilitates automated, real-time routing, transformation, and mediation of data between disparate systems, supported by a web-based user interface and granular control mechanisms.

This instructor-led live training, available either onsite or remotely, targets intermediate-level administrators and engineers looking to deploy, manage, secure, and optimize NiFi dataflows within production environments.

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

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

Course Format

• Interactive lectures combined with discussions on real-world architectures.
• Practical labs focused on building, deploying, and managing data flows.
• Scenario-based exercises conducted in a live laboratory environment.

Course Customization Options

• For customized training arrangements, please contact us.

In this instructor-led, live training in Czech Republic, 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 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 Czech Republic, 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 Czech Republic (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 unifies big data, AI, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led, live training (available online or onsite) is designed for intermediate-level data professionals who want to effectively leverage the Rocket and Intelligence modules in Stratio with 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 work efficiently 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 feature engineering processes.
• Create and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

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

Course Customization Options

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