Data Streaming and Real Time Data Processing Training Course

Audience:

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

Goal:

To provide in-depth knowledge on administering Hadoop clusters.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use 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 analyzing large volumes of diverse data sets to identify correlations, hidden patterns, and other valuable insights.

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

Through this instructor-led live training (conducted remotely), participants will learn how to perform big data analytics in healthcare by completing a series of hands-on lab exercises.

By the end 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 healthcare applications

Audience

• Developers
• Data Scientists

Format of the Course

• A combination 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. During this three-day course (with a four-day option available), participants will explore the business advantages and practical applications of Hadoop and its broader ecosystem. The curriculum covers cluster deployment planning and scalability strategies, as well as the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop environments. Attendees will gain hands-on experience with bulk data loading, familiarize themselves with various Hadoop distributions, and practice installing and managing ecosystem tools. The course concludes with a session 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

Target Audience

Hadoop system administrators

Course Format

A blend of lectures and hands-on labs, with an approximate split of 60% lecture content and 40% practical lab work.

Apache Hadoop stands as the leading framework for processing Big Data across clusters of servers. This course is designed to familiarize developers with key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands out as one of the most widely adopted frameworks for processing Big Data across server clusters. This course offers an in-depth exploration of data management within HDFS, alongside advanced usage of Pig, Hive, and HBase. These sophisticated programming techniques are particularly valuable for seasoned Hadoop developers.

Audience: developers

Duration: three days

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

Target Audience:

This course is designed to demystify Big Data and Hadoop technologies, demonstrating that understanding them is more accessible than one might think.

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

Upon completing this training, participants will be capable of:

• Installing and configuring Apache Hadoop.
• Gaining a comprehensive understanding of the four core components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Leveraging the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configuring HDFS to serve as the storage engine for on-premise Spark deployments.
• Configuring Spark to access alternative storage solutions, including Amazon S3 and NoSQL databases such as Redis, Elasticsearch, Couchbase, and Aerospike.
• Performing essential administrative tasks such as provisioning, management, monitoring, and securing an Apache Hadoop cluster.

This course offers 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's architecture, data modeling principles, and application development practices. It also covers the integration of MapReduce with HBase and addresses key administrative topics focused on performance optimization. The course is highly practical, featuring numerous hands-on 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 Bulgaria, 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 with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to effectively manage large datasets by leveraging distributed computing principles.

In this instructor-led, live training in Bulgaria, 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 Bulgaria (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 comprehensive, data-driven platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and sophisticated analytics within enterprise settings.

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

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

• Efficiently navigate and utilize the Stratio platform, specifically the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analytical processes.
• Implement loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to create reusable data operations within PySpark.

Training Format

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

Customization Options

• For customized training arrangements for this course, please contact us directly.