Big Data Analytics in Health 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.

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.

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

Upon completion 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 suitable framework for specific tasks.
• Process data continuously, concurrently, and record-by-record.
• 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 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.

This instructor-led, live training in Taiwan (online or onsite) is targeted at data scientists who aim 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) targets beginner 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 before achieving tangible results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDD and DataFrame, and gain proficiency in both Python and Scala APIs. You will also gain a solid understanding of executors, tasks, and other core concepts. Adhering to industry best practices, the course places a strong emphasis on cloud deployment, specifically within Databricks and AWS environments. Students will also learn to differentiate 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 provides an introduction to Apache Spark. Participants will discover how Spark integrates into the Big Data ecosystem and learn to leverage it for data analysis. The curriculum encompasses the Spark shell for interactive analytics, Spark internals, APIs, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX.

AUDIENCE:

Developers and Data Analysts

This instructor-led, live training in Taiwan (online or on-site) is tailored for data scientists and developers who aim to leverage Spark NLP, which is 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 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.

Spark SQL serves as Apache Spark's module designed for handling both structured and unstructured data. It offers insights into data structure and the computations involved, enabling performance optimizations. Spark SQL is commonly utilized for:
- Executing SQL queries.
- Accessing data from existing Hive installations.

In this instructor-led live training, available either onsite or remotely, participants will learn to analyze diverse datasets using Spark SQL.

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

• Install and configure Spark SQL.
• Conduct data analysis using Spark SQL.
• Query datasets in various formats.
• Visualize data and query outcomes.

Course Format

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

Course Customization Options

• To arrange a customized training session for this course, please contact us directly.

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.