Big Data Analytics in Health Training Course

Audience:

This course is designed for IT professionals who are seeking a solution to store and process large datasets in a distributed system environment.

Goal:

To gain an in-depth understanding of Hadoop cluster administration.

This instructor-led, live training in Taiwan (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.

Apache Hadoop is the most widely used framework for processing Big Data on clusters of servers. In this three-day (or optionally four-day) course, participants will gain insights into the business benefits and use cases for Hadoop and its ecosystem. They will learn how to plan cluster deployment and expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop. The course also includes practical exercises on bulk data loading, familiarizing attendees with various Hadoop distributions, and 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 extremely helpful and well-organized.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

The course combines lectures with hands-on labs, maintaining an approximate balance of 60% lectures and 40% labs.

This instructor-led, live training in Taiwan (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.

In this instructor-led, live training in Taiwan (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

By the end of this training, participants will be able to:

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process of data continuously, concurrently, and in a record-by-record fashion.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This instructor-led, live training in Taiwan (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.

By the end of this training, participants will be able to:

• Implement a data pipeline architecture for processing big data.
• Develop a cluster infrastructure with Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in Taiwan (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyze very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Taiwan (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.

By the end of this training, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

The initial learning curve for Apache Spark can be steep, requiring significant effort to achieve the first tangible results. This course is designed to help participants navigate through this challenging phase. By the end of the course, participants will have a solid understanding of the fundamentals of Apache Spark, including the differences between RDD and DataFrame. They will also become familiar with both Python and Scala APIs, gain insights into executors and tasks, and more. Following best practices, the course places a strong emphasis on cloud deployment, particularly with Databricks and AWS. Additionally, students will learn to distinguish between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course will provide an introduction to Apache Spark. Students will gain an understanding of how Spark integrates into the Big Data ecosystem and learn to leverage Spark for data analysis. The curriculum includes interactive data analysis using the Spark shell, an exploration of Spark's internal mechanisms, its APIs, Spark SQL, Spark Streaming, as well as machine learning and GraphX.

AUDIENCE :

Developers / Data Analysts

This instructor-led, live training in Taiwan (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Use the pre-trained models available in Spark NLP to implement text processing.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led, live training in Taiwan, 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 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.

Spark SQL is an Apache Spark module designed for handling both structured and unstructured data. It provides insights into the structure of the data and the computations being performed, which can be leveraged for optimization. Two common applications of Spark SQL include:
- Executing SQL queries.
- Reading data from an existing Hive installation.

In this instructor-led, live training (either on-site or remote), participants will learn how to analyze various types of datasets using Spark SQL.

By the end of this training, participants will be able to:

• Install and configure Spark SQL.
• Perform data analysis with Spark SQL.
• Query datasets in different formats.
• Visualize data and query results.

Format of the Course

• Interactive lectures and discussions.
• Plenty of exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

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

Stratio is a data-centric platform that seamlessly integrates big data, AI, and governance into a unified solution. Its Rocket and Intelligence modules facilitate efficient data exploration, transformation, and advanced analytics in enterprise settings.

This instructor-led, live training (available online or on-site) is designed for intermediate-level data professionals who aim to leverage the Rocket and Intelligence modules of Stratio effectively with PySpark. The focus will be on looping structures, user-defined functions, and advanced data logic.

By the end of this training, participants will be able to:

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

Format of the Course

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

Course Customization Options

• For a customized training experience, please contact us to arrange.