Data Streaming and Real Time Data Processing Training Course

Audience:

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

Objective:

To provide in-depth knowledge of Hadoop cluster administration.

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

By the conclusion 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 is the process of examining vast volumes of diverse data sets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare industry generates massive amounts 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 considerable challenges for analysis and practical application within clinical environments.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health 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 handle medical data
• Study big data systems and algorithms in 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 a 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 real-world applications of Hadoop and its surrounding ecosystem. Attendees will gain skills in planning cluster deployment and scalability, as well as in installing, maintaining, monitoring, troubleshooting, and optimizing Hadoop environments. Practical exercises include bulk data loading, familiarization with various Hadoop distributions, and the installation and management of 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 combination of lectures and hands-on labs, with an approximate split of 60% lectures and 40% labs.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course introduces developers to key components within the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands out as one of the leading frameworks for processing Big Data across server clusters. This course explores data management within HDFS, alongside advanced techniques for Pig, Hive, and HBase. These specialized programming skills will be particularly valuable for experienced 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 they are accessible and easy to understand.

This instructor-led live training in Brazil (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 introduces HBase—a NoSQL data store built on top of Hadoop. It is designed for developers who will be building applications with HBase, as well as administrators responsible for managing HBase clusters.

Participants will explore HBase architecture, data modeling, and application development techniques. The curriculum also covers integrating MapReduce with HBase and addresses key administration topics, particularly focusing on performance optimization. The course 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 Brazil, participants will learn the fundamentals of flow-based programming as they develop several 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 creating scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets by leveraging distributed computing principles.

In this instructor-led, live training in Brazil, 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 Brazil (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 environments.

This instructor-led live training (available online or onsite) is designed for intermediate-level data professionals seeking to effectively utilize the Rocket and Intelligence modules in Stratio with PySpark. The course focuses on looping structures, user-defined functions, and advanced data logic.

Upon completing this training, participants will be able to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and perform feature engineering.
• Create and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

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

Course Customization Options

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