Big Data Analytics in Health Training Course

Target Audience:

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

Learning Objectives:

To acquire in-depth knowledge regarding Hadoop cluster administration.

This instructor-led, live training in the UAE (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 the UAE (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 major components in the Hadoop ecosystem: 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 the UAE (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.

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 suitable framework for specific tasks.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and more.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

This instructor-led live training, conducted the UAE (either online or onsite), is intended for data scientists who wish to leverage the SMACK stack to develop data processing platforms for big data solutions.

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

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

This instructor-led, live training in the UAE (online or onsite) 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 the UAE (online or onsite) is designed for system administrators at the beginner to intermediate level 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 learning curve for Apache Spark can be steep initially, often requiring significant effort before yielding tangible results. This course is designed to help you navigate that challenging early phase. By the end of the course, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency in both Python and Scala APIs. Learners will also develop a solid understanding of executors, tasks, and other core concepts. Aligning with industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Additionally, students will learn to differentiate between AWS EMR and AWS Glue, highlighting one of AWS's most recent Spark-related services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will gain insights into where Spark fits within the Big Data ecosystem and how to leverage it for data analysis. The curriculum includes hands-on training with the Spark shell for interactive analysis, an exploration of Spark internals, API usage, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX.

AUDIENCE :

Developers and Data Analysts

This instructor-led live training, conducted in the UAE (either online or onsite), 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 the UAE, 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 the UAE (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 dedicated module for handling both structured and unstructured data. By exposing details about data structure and the computations being executed, it enables various optimization strategies. Spark SQL is primarily utilized for two key purposes:
- Executing SQL queries.
- Reading data from an existing Hive installation.

Through this instructor-led live training, available either onsite or remotely, participants will acquire the skills to analyze diverse datasets using Spark SQL.

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

• Installing and setting up Spark SQL.
• Conducting data analysis with Spark SQL.
• Querying datasets across various formats.
• Visualizing data and the resulting queries.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Practical implementation within a live-lab environment.

Customization Options

• For requests regarding customized training for this course, please reach out to us to make arrangements.

Stratio serves as a comprehensive, data-centric platform that unifies big data, artificial intelligence, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training (available online or on-site) targets intermediate-level data professionals aiming to leverage the Rocket and Intelligence modules in Stratio effectively with PySpark. The curriculum focuses on looping structures, user-defined functions, and advanced data logic.

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

• Navigating and operating within the Stratio platform using the Rocket and Intelligence modules.
• Applying PySpark for data ingestion, transformation, and analysis.
• Utilizing loops and conditional logic to manage data workflows and feature engineering tasks.
• Creating and managing user-defined functions (UDFs) for reusable data operations in PySpark.

Course Format

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

Customization Options

• To request customized training for this course, please contact us to make arrangements.