Big Data Analytics in Health Training Course

Audience:

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

Objective:

To acquire in-depth expertise in administering Hadoop clusters.

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

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

• Establish a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training in Greece (online or onsite) targets 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 Greece (onsite or remote), attendees will discover how to configure and connect various Stream Processing frameworks with existing big data storage systems, as well as related software applications and microservices.

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

• Install and configure diverse Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand the options and choose the most suitable framework for specific requirements.
• Handle data continuously, concurrently, and record-by-record.
• Connect Stream Processing solutions with current databases, data warehouses, data lakes, and other systems.
• Integrate the most fitting stream processing library into enterprise applications and microservices.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This instructor-led live training in Greece (online or onsite) is designed for data scientists who wish to leverage the SMACK stack to build data processing platforms for big data solutions.

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

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

This instructor-led live training in Greece (online or on-site) is aimed at engineers who wish to set up and deploy an 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 Greece (online or onsite) targets beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across diverse environments.
• Manage cluster resources and monitor Spark applications.
• Enhance the performance of Spark clusters.
• Implement security protocols and ensure high availability.
• Debug and resolve common Spark issues.

While Apache Spark initially presents a steep learning curve requiring considerable effort to yield early results, this course is designed to help learners navigate and overcome that initial hurdle. Upon completion, participants will gain a solid grasp of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, become proficient with the Python and Scala APIs, and understand core concepts such as executors and tasks. Aligned with industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically targeting Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, which represents one of AWS's newer Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will learn how Spark integrates into the Big Data ecosystem and gain the skills to perform data analysis using Spark. The curriculum covers the Spark shell for interactive analysis, internal mechanisms, Spark APIs, Spark SQL, Spark Streaming, as well as machine learning and GraphX capabilities.

AUDIENCE :

Developers / Data Analysts

This instructor-led live training in Greece (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:

• 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 Greece, participants will learn how to leverage Python and Spark together to analyze big data while completing hands-on exercises.

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

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

This instructor-led live training in Greece (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 the module within Apache Spark designed for processing both structured and unstructured data. It offers insights into the data's structure and the computations being executed, enabling performance optimizations. Spark SQL is commonly utilized to:
- execute SQL queries;
- access data from existing Hive installations.

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

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

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

Course Format

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

Customization Options

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

Stratio offers a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise-grade environments.

This instructor-led live training, available online or on-site, is designed for intermediate-level data professionals seeking to harness the full potential of Stratio’s Rocket and Intelligence modules using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic to enhance workflow efficiency.

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

• Navigate and effectively utilize the Stratio platform, specifically the 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 execute feature engineering tasks.
• Develop and manage User-Defined Functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures paired with group discussions.
• Extensive hands-on exercises and practical practice sessions.
• Real-world implementation through a live-lab environment.

Customization Options

• For tailored training solutions, please reach out to us to discuss your specific requirements.