Big Data Analytics in Health Training Course

Audience:

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

Goal:

To develop in-depth knowledge of Hadoop cluster administration.

This instructor-led live training in Italy (online or onsite) is intended for intermediate-level data scientists and engineers seeking to utilize Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be capable of:

• Establishing a big data environment using Google Colab and Spark.
• Efficiently processing and analyzing large-scale datasets with Apache Spark.
• Visualizing big data within a collaborative framework.
• Integrating Apache Spark with cloud-based tools.

This instructor-led, live training in Italy (online or onsite) is designed for 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 key components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Utilize the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to serve as the storage engine for on-premise Spark deployments.
• Configure Spark to access alternative storage solutions, such as Amazon S3, and NoSQL database systems like Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform administrative tasks, including provisioning, management, monitoring, and securing an Apache Hadoop cluster.

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

Upon completing this training, participants will be able to:

• Install and configure various Stream Processing frameworks, including Spark Streaming and Kafka Streaming.
• Understand and choose the most suitable framework for specific requirements.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and other systems.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by leveraging distributed computing principles.

This instructor-led, live training in Italy (available online or onsite) is tailored for data scientists who aim to utilize the SMACK stack to build data processing platforms for big data solutions.

By the conclusion of this training, participants will be capable of:

• Implementing a data pipeline architecture for big data processing.
• Developing cluster infrastructure with Apache Mesos and Docker.
• Analyzing data using Spark and Scala.
• Managing unstructured data with Apache Cassandra.

This instructor-led, live training in Italy (online or onsite) is designed for engineers who want to set up and deploy an Apache Spark system for processing vast 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 datasets.
• Understand the distinctions between Apache Spark and Hadoop MapReduce, and identify when to use each.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Italy (online or onsite) targets beginner to intermediate 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 considerable effort before yielding tangible results. This course is designed to help learners navigate this challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. They will also gain insight into executors, tasks, and other core concepts. Aligned with best practices, the course places a strong emphasis on cloud deployment, with specific attention to Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's more recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark, helping students understand its role within the Big Data ecosystem and how to effectively apply it for data analysis. The curriculum includes hands-on exploration of the Spark shell for interactive analysis, a deep dive into Spark internals, comprehensive coverage of Spark APIs, and extensive training on Spark SQL, Spark Streaming, Machine Learning, and GraphX.

AUDIENCE :

Developers / Data Analysts

This instructor-led live training in Italy (online or on-site) is designed for 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:

• Configure the necessary development environment to begin building NLP pipelines with Spark NLP.
• Gain a clear understanding of Spark NLP’s features, architecture, and key benefits.
• Utilize pre-trained models available in Spark NLP to execute text processing tasks.
• Learn how to build, train, and scale Spark NLP models suitable for production-grade projects.
• Apply classification, inference, and sentiment analysis to real-world scenarios (such as clinical data analysis and customer behavior insights).

In this instructor-led, live training in Italy, participants will learn how to combine Python and Spark to analyze big data while engaging in 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 Italy (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 represents Apache Spark’s module designed for managing structured and unstructured data. It offers insights into the data structure as well as the computations being executed, enabling the system to perform optimizations. Spark SQL is commonly utilized for two primary purposes: executing SQL queries and accessing data from an existing Hive installation.

Through this instructor-led live training (available on-site or remotely), participants will gain the skills to analyze diverse data sets using Spark SQL.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

• For those seeking a tailored training experience, please contact us to arrange it.

Stratio serves as a unified, data-centric platform that seamlessly combines big data capabilities, artificial intelligence, and governance. Its Rocket and Intelligence modules empower organizations to rapidly explore, transform, and analyze data with advanced analytics tailored for enterprise needs.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to master the Rocket and Intelligence modules within Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and sophisticated data logic.

Upon completion, participants will gain the ability to:

• Effectively navigate and utilize the Rocket and Intelligence modules within the Stratio platform.
• Implement PySpark for efficient data ingestion, transformation, and analytical processes.
• Control data workflows and execute feature engineering tasks using loops and conditional logic.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Training Format

• Engaging interactive lectures and discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For tailored training requests, please contact us to make arrangements.