Big Data Analytics in Health Training Course

This course targets developers and data scientists interested in integrating AI into their applications. It places particular emphasis on Data Analysis, Distributed AI, and Natural Language Processing.

In this instructor-led, live training in the UAE, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.

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

• Understand how graph data is persisted and traversed.
• Select the best framework for a given task (from graph databases to batch processing frameworks.)
• Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
• View real-world big data problems in terms of graphs, processes and traversals.

This instructor-led, live training in the UAE (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.

This instructor-led, live training in the UAE (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.

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

• Use Hortonworks to reliably run Hadoop at a large scale.
• Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
• Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
• Process different types of data, including structured, unstructured, in-motion, and at-rest.

This course focuses on utilizing the Hive SQL language (also known as Hive HQL, SQL on Hive, or HiveQL) for individuals who need to extract data from Hive.

Cloudera Impala is an open-source, massively parallel processing (MPP) SQL query engine designed for Apache Hadoop clusters.

Impala allows users to execute low-latency SQL queries on data stored in the Hadoop Distributed File System and Apache HBase without needing to move or transform the data.

Audience

This course is intended for analysts and data scientists who analyze data stored in Hadoop using Business Intelligence or SQL tools.

Upon completion of this course, participants will be able to

• Extract valuable insights from Hadoop clusters using Impala.
• Create specific programs to support Business Intelligence with Impala's SQL dialect.
• Troubleshoot issues in Impala.

In this instructor-led, live training in the UAE (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 the UAE (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 the UAE (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.

The initial learning curve for Apache Spark can be steep, requiring significant effort to achieve the first results. This course is designed to help participants navigate through this challenging phase. By the end of the course, attendees will have a solid grasp of the fundamentals of Apache Spark, including the distinction between RDD and DataFrame. They will also become proficient in using both Python and Scala APIs, gain an understanding of executors and tasks, and more. Additionally, adhering to best practices, this course places strong emphasis on cloud deployment strategies, with a focus on Databricks and AWS. Students will learn to differentiate between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Specialists, Data Scientists

OBJECTIVE:

This course will provide an introduction to Apache Spark. Students will learn how Spark integrates into the Big Data ecosystem and how to utilize Spark for data analysis. The curriculum includes using the Spark shell for interactive data analysis, understanding Spark's internal workings, working with Spark APIs, leveraging Spark SQL, implementing Spark streaming, and applying machine learning and graphX.

AUDIENCE :

Developers / Data Analysts

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.

Spark SQL is the module within Apache Spark designed for handling both structured and unstructured data. It offers insights into the structure of the data and the computations being executed, which can be leveraged for optimization purposes. Two primary applications of Spark SQL include:
- Executing SQL queries.
- Accessing data from an existing Hive setup.

In this instructor-led training session (conducted either on-site or remotely), participants will gain skills in analyzing diverse datasets using Spark SQL.

By the conclusion of this course, attendees will be equipped to:

• Set up and configure Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets available in various formats.
• Visualize data and the outcomes of queries.

Course Format

• An interactive lecture combined with discussion sessions.
• A multitude of exercises and practical applications.
• Hands-on implementation in a live-lab setting.

Customization Options for the Course

• To tailor this course to specific needs, please reach out to us to discuss your requirements.

MLlib serves as the machine learning (ML) library for Spark, aiming to make scalable and user-friendly practical machine learning accessible. It encompasses various learning algorithms and utilities such as classification, regression, clustering, collaborative filtering, dimensionality reduction, along with foundational optimization tools and advanced pipeline APIs.

The library is divided into two main packages:

• spark.mllib includes the original API constructed using RDDs.

• spark.ml offers a more sophisticated API based on DataFrames, facilitating the creation of ML pipelines.

Audience

This course is tailored for engineers and developers looking to leverage an integrated Machine Learning Library within Apache Spark.