DATA WAREHOUSING ON AWS

Course Overview

In this course, you will learn new concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. You will learn how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3. Additionally, this course demonstrates how to use business intelligence tools to perform analysis on your data.

Course Objectives

After completing this course, students will be able to:

    • Core concepts of data warehousing
    • Evaluate the relationship between Amazon Redshift and other big data systems
    • Evaluate use cases for data warehousing workloads and review case studies that demonstrate implementation of AWS data and analytic services as part of a data warehousing solution
    • Choose an appropriate Amazon Redshift node type and size for your data needs
    • Discuss security features as they pertain to Amazon Redshift, such as encryption, IAM permissions, and database permissions
    • Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud
    • Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3, to contribute to the data warehousing solution
    • Approaches and methodologies for designing data warehouses
    • Data sources and assess requirements that affect the data warehouse design
    • Design the data warehouse to make effective 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
    • Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing
    • Use features and services, such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon CloudWatch, and Amazon Simple Notification Service (Amazon SNS), to audit, monitor, and receive event notifications about activities in the data warehouse
    • Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters
    • Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data

Course Prequisites

 
  • AWS Technical Essentials
  • familiarity with relational databases and database design concepts

Target Audience

This course is intended for:

  • Database architects
  • Database administrators
  • Database developers
  • Data analysts
  • Data scientists
  • Lesson 1 : Course Introduction

  • Lesson 2 : Introduction to Data Warehousing

  • Lesson 3 : Introduction to Amazon Redshift

  • Lesson 4 : Understanding Amazon Redshift Components and Resources

  • Lesson 5 : Launching an Amazon Redshift Cluster

  • Lesson 6 : Reviewing Data Warehousing Approaches

  • Lesson 7 : Identifying Data Sources and Requirements

  • Lesson 8 : Designing the Data Warehouse

  • Lesson 9 : Loading Data into the Data Warehouse

  • Lesson 10 : Writing Queries and Tuning Performance

  • Lesson 11 : Maintaining the Data Warehouse

  • Lesson 12 : Analyzing and Visualizing Data

  • Lesson 13 : Course Summary

What Our Students Have to Say

"As a client of Makintouch, I would recommend the company as a leading PC-based training school. When we needed half day courses to fit the schedules of our employees they were able to customize them to meet our company’s needs" – Java Struts
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I would recommend this course as it is up to date with the current release which is quite rare as things move on so fast. The classroom set up worked well and the labs were good and relevant to the course.’ – Oracle
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