Big Data - Data Science Training Course

Target Audience:

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

Course Objectives:

To provide in-depth knowledge regarding Hadoop cluster administration.

This instructor-led live training in Taiwan (online or onsite) is tailored for 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 entails the process of scrutinizing extensive and diverse datasets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare sector generates massive volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics on this health data holds significant potential for deriving insights that can enhance healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical application within clinical environments.

In this instructor-led, live training (delivered remotely), participants will learn how to conduct big data analytics in health by engaging in a series of hands-on, live-lab 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 unique characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Study 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 arrange.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This three-day course (with an optional fourth day) enables participants to understand the business advantages and practical use cases of Hadoop and its ecosystem. Attendees will learn to plan cluster deployment and scalability, as well as install, maintain, monitor, troubleshoot, and optimize Hadoop environments. The curriculum includes hands-on practice with bulk data loading, exploration of various Hadoop distributions, and management of Hadoop ecosystem tools. The course concludes with a discussion on securing the cluster 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 balance of 60% lectures and 40% labs.

Apache Hadoop stands as the most widely adopted framework for processing Big Data across server clusters. This course provides developers with an introduction to the various components within 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 explores data management within HDFS, alongside advanced techniques for Pig, Hive, and HBase. These sophisticated programming strategies are designed to be 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 they are accessible and not overly complex to grasp.

This instructor-led, live training in Taiwan (online or on-site) 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 of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Set up HDFS to operate as a 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 database built on top of Hadoop. It is designed for developers who plan to build applications using HBase, as well as administrators responsible for managing HBase clusters.

The curriculum guides developers through HBase’s architecture, data modeling techniques, and application development processes. It also covers the integration of MapReduce with HBase and addresses administrative topics related to 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 held in Taiwan, participants will master the fundamentals of flow-based programming by developing numerous 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 using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by applying distributed computing principles.

In this instructor-led, live training in Taiwan, participants will learn how to combine Python and Spark 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 Taiwan (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.