Course Outline
Lessons
Validate new skills and apply knowledge to your working environment through a variety of practical exercises.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.
What You'll Learn
• 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
Who Needs to Attend
• Database architects
• Database administrators
• Database developers
• Data analysts and scientists
Outline
This course covers the following concepts
Day 1
• Course Introduction
• Introduction to Data Warehousing
• Introduction to Amazon Redshift
• Understanding Amazon Redshift Components and Resources
• Launching an Amazon Redshift Cluster
Day 2
• Reviewing Data Warehousing Approaches
• Identifying Data Sources and Requirements
• Designing the Data Warehouse
• Loading Data into the Data Warehouse
Day 3
• Writing Queries and Tuning Performance
• Maintaining the Data Warehouse
• Analyzing and Visualizing Data
• Course Summary
Cancellation Policy
We require 16 calendar days notice to reschedule or cancel any registration. Failure to provide the required notification will result in 100% charge of the course. If a student does not attend a scheduled course without prior notification it will result in full forfeiture of the funds and no reschedule will be allowed. Within the required notification period, only student substitutions will be permitted. Reschedules are permitted at anytime with 16 or more calendar days notice. Enrollments must be rescheduled within six months of the cancel date or funds on account will be forfeited.
Training Location
Online Classroom
your office
your city,
your province
your country
I would never take another course that starts at 11AM and goes to 9PM again. The way the course was laid out really took away from the capturing of what was presented as it was 5-6 hours of watching a screen before getting to the actual labs. There has to be a better way to lay out this particular course. In my previous course, the lectures were broken up by labs which worked out fantastic and kept you engaged in the course. There were days when in order to actually complete the labs, would go over the 9PM day end time frame. Was able to get the primary labs done, but if you want to get all the content completed, you cannot complete it in the window of this course, you will need to come back on your own time.