Overview What You Will Learn Curriculum Instructor

Course Overview

Data is the new oil. 

Data is becoming the science and the art of making insightful decisions for every organization from healthcare, sciences, agriculture, financial services, telecommunications, and political decisions.

The data is in the details and all the decision that you have to make is based on the patterns that emerge in the data that you collect and all this begins with setting up your Analytical Massively Parallel Processing (MPP) Databases with Amazon Redshift that is  already optimized for managing analytical workloads.

In this course, you will learn how to build a data warehouse using Amazon’s Data Warehouse As A Service (Redshift). More importantly, you will learn the difference between OLTP (Online Transaction Processing systems) and OLAP (Online Analytical Processing Systems).

You will also learn how to build clusters of 2 or more nodes of Massively Parallel Processing Data Warehouse systems and load data from multiple databases into your Amazon Redshift Cluster.

You will also manage the data and generate analysis, without compromising the security of your data and system of records, while running on one of the most cloud systems in the world.

What You Will Learn

  • Analyze and Discuss the core concepts of data warehousing
  • Understand the relationship between Amazon Redshift and other big data systems
  • Implement data warehousing use cases and review case studies that demonstrate implementation of AWS data warehousing solutions
  • Size your Amazon Redshift node type to meet your present and future data requirements
  • Review security features that exists on Amazon Redshift
  • such as IAM permissions
  • encryption and database permissions
  • Launch an Amazon Redshift cluster and use the components
  • features
  • and functionality to implement a data warehouse in the cloud
  • Review various approaches and methodologies used for designing data warehouses
  • Identify data sources and assess requirements that affect the data warehouse design
  • Design the warehouse to make use of compression
  • data distribution
  • and sort methods
  • Load and unload data and perform data maintenance tasks
  • Write queries and evaluate query plans to optimize query performance
  • Prepare for operational tasks
  • such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters

Program Curriculum

  • Introduction to Data, Big Data, Fast Data, Small Data
  • Introduction to Data Warehousing
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • History of Amazon Redshift
  • Architecture of Warehouse Systems
  • Introduction to Amazon Redshift
  • Launching Amazon Redshift Clusters
  • Chapter 2 Quiz

  • Designing the Database Schema
  • Identifying the Data Sources
  • Table Setup
  • Column-oriented Storage
  • Chapter 3 Quiz

  • Introduction to ETL (Extract, Transform and Load)
  • Loading Data
  • Using the Copy Command
  • Writing Queries, Functions and Views
  • Using Amazon Redshift Spectrum to Query Data
  • Using Amazon Redshift Spectrum to Query Data
  • Analyzing and Visualizing Data
  • Chapter 4 Quiz

  • AWS Shared Responsibility
  • Securing Your Root Accounts on the Amazon Cloud
  • Enable Cluster Encryption When Launching Redshift Cluster
  • Implementing Role Based Access Control using Users, Groups, Grant and Revoke Privileges
  • Chapter 5 Quiz

  • Backup Concepts
  • Maintaining Clusters
  • Cluster Backups
  • Chapter 6 Quiz

  • Introduction to Tuning
  • Tuning Performance
  • Chapter 7 Quiz

  • Key Takeaways
  • Thank you and Contact Information
Load more modules

Instructor

Michael Onuorah

Michael Onuorah has worked with Databases, Data Warehouses and Big Data Systems in various over the last 20 years. His experience has been in deploying databases, big data systems and data warehouses, focusing on systems of records, systems of engagement, while improving data operations, and transforming data flow within organizations. He has certifications from the Project Management Institute, Oracle, Amazon, NoSQL databases and in Lean Six Sigma. He has also been teaching and managing databases, big data and data warehouse systems and he believes that data is the interconnecting factor across all Industry 4.0 technologies. Michael sees data as the future, however, he strongly believes that the data is the detail, so we must mine and analyze the data until it gives us the relevant insight that we need.

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