Big Data Analytics in Health Training Course

Target Audience:

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

Objective:

To provide in-depth knowledge and expertise in Hadoop cluster administration.

This instructor-led, live training in France (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.

This instructor-led, live training in France (online or onsite) is designed for system administrators who wish to learn how to set up, deploy, and manage Hadoop clusters within their organization.

Upon completing 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.
• Leverage the Hadoop Distributed File System (HDFS) to scale a cluster across 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, including Amazon S3 and NoSQL databases such as Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform essential administrative tasks, including provisioning, management, monitoring, and securing an Apache Hadoop cluster.

In this instructor-led, live training in France (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, 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 specific tasks.
• Process data continuously, concurrently, and record-by-record.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and more.
• Integrate the most suitable 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 how to effectively manage large datasets by applying distributed computing principles.

This instructor-led live training in France (online or onsite) targets data scientists who aim to use the SMACK stack to build data processing platforms for big data solutions.

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

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

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

Upon completion of this training, participants will be capable of:

• Installing and configuring Apache Spark across various environments.
• Managing cluster resources and monitoring Spark applications.
• Optimizing the performance of Spark clusters.
• Implementing security measures and ensuring high availability.
• Debugging and troubleshooting common Spark issues.

Although Apache Spark has a challenging initial learning curve that requires significant effort to yield early results, this course is designed to help you navigate that difficult beginning. Upon completion, participants will gain a solid understanding of Apache Spark fundamentals, including the ability to clearly distinguish between RDDs and DataFrames. The curriculum covers the Python and Scala APIs, as well as key concepts such as executors and tasks. Aligned with best practices, the course emphasizes cloud deployment, with a strong focus on Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Students will understand how Spark integrates into the Big Data ecosystem and learn to utilize it for data analysis. The curriculum includes the Spark shell for interactive analysis, Spark internals, APIs, Spark SQL, Spark Streaming, Machine Learning, and GraphX.

AUDIENCE :

Developers / Data Analysts

This instructor-led live training in France (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:

• Configure the necessary development environment to begin building NLP pipelines with Spark NLP.
• Gain a comprehensive understanding of Spark NLP's features, architecture, and benefits.
• Utilize pre-trained models available in Spark NLP to implement text processing tasks.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis to real-world use cases (e.g., clinical data, customer behavior insights).

In this instructor-led, live training in France, participants will discover how to leverage Python and Spark in unison to analyze big data through hands-on exercises.

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

• Understand how to use Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world scenarios.
• Utilize various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in France (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 the component of Apache Spark designed for processing structured and semi-structured data. It provides detailed metadata regarding data structure and the computations involved, enabling performance optimizations. The primary applications of Spark SQL include:
- Executing SQL queries.
- Accessing data from existing Hive installations.

This instructor-led live training, available both onsite and remotely, guides participants through the analysis of diverse data sets using Spark SQL.

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

• Install and set up Spark SQL.
• Conduct data analysis using Spark SQL.
• Query data sets in various formats.
• Visualize data and query outcomes.

Course Format

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

Customization Options

• To arrange a customized version of this course, please contact us directly.

Stratio is a data-centric platform that seamlessly integrates big data, AI, and governance into a single, unified solution. Its Rocket and Intelligence modules empower organizations with rapid data exploration, transformation, and advanced analytics capabilities tailored for enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals looking to master the Rocket and Intelligence modules within Stratio using PySpark. The curriculum focuses on leveraging looping structures, user-defined functions, and complex data logic to enhance workflow efficiency.

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

• Navigate and effectively utilize the Stratio platform through its Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis within the Stratio ecosystem.
• Implement loops and conditional logic to manage data workflows and streamline feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to create reusable data operations in PySpark.

Course Format

• Engaging interactive lectures and discussions.
• Extensive exercises and practical 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.