Big Data Analytics in Health Training Course

Audience:

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

Goal:

To gain in-depth knowledge of Hadoop cluster administration.

This instructor-led, live training in Slovakia (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.

Audience:

This course aims to simplify big data and Hadoop technology, demonstrating that these concepts are not as complex as they might seem.

This instructor-led, live training in Slovakia (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 Slovakia (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 Slovakia (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 Slovakia (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 Slovakia (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 quite steep, requiring significant effort to achieve the first tangible results. This course is designed to help participants navigate through this challenging initial phase. By the end of the course, attendees will have a solid understanding of Apache Spark's fundamentals, including the distinction between RDD and DataFrame. They will also become proficient in using Python and Scala APIs, gain insights into executors and tasks, and more. The course emphasizes best practices, with a strong focus on cloud deployment, particularly using Databricks and AWS. Participants will learn to differentiate between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineer, DevOps, Data Scientist

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 use Spark for data analysis. The curriculum covers interactive data analysis with the Spark shell, Spark's internal mechanisms, its APIs, Spark SQL, Spark streaming, and machine learning using GraphX.

AUDIENCE :

Developers / Data Analysts

This instructor-led, live training in Slovakia (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 Slovakia, 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 Slovakia (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 Apache Spark's module designed for handling both structured and unstructured data. It provides insights into the structure of the data as well as the computations being executed, which can be leveraged for optimization. Two common applications of Spark SQL include:
- executing SQL queries.
- accessing data from an existing Hive installation.

In this instructor-led, live training (either onsite 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.
• Conduct data analysis using Spark SQL.
• Query datasets in different formats.
• Visualize data and query results.

Course Format

• Interactive lecture and discussion.
• Ample exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options for the Course

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

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

This instructor-led, live training (available online or on-site) is designed for intermediate-level data professionals who want to effectively use the Rocket and Intelligence modules of Stratio 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 the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Use loops and conditional logic to manage 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.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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