Data analytics on AWS: index

If you have decided to use AWS for research data management related activities, the University Libraries aims to provide professional consulting services (faculty, staff, graduate and undergraduate students) on how to use AWS to its full potential. The team members have been certified by Amazon as Developer, SysOps Administrator, and Solution Architect, and have won multiple Amazon teaching and research awards. We will:

1. Share experience on conducting research data analysis and experiments on AWS
2. Provide best practices guidance on data research projects
3. Review research proposal (AWS Cloud Credits for Research, AWS research awards, etc) 

Consultation online request form: https://goo.gl/forms/k4OZR8vfVZ3HHthq1 

Users should consult with Collaborative Computing Solutions (CCS) within the Division of IT (DoIT) concerning your project needs, AWS Procurement and Licensing, and security and compliance requirements. Users should also consult with VT's Information Security Office with any questions or concerns about IT or data security.

This course provides an introduction to AWS and offers a broad overview of terminology and concepts. The course will focus on the AWS web console, CLI tools, and data storage needed to be successful in data management on AWS. This course is intended as a prerequisite for the companion NLI course, Data Analytics using Amazon Web Services (AWS).

This course will first give a broad overview of AWS terminology while giving a tour of the AWS web console. We will talk about users and user permissions and introduce the concept of least privilege. We will introduce the AWS CLI tools, and give an overview of what they can be used for. Then we will learn how to use these tools to do basic tasks like storing data and giving a user access to that data.

Current NLI class:

• 02/15/18: Amazon Web Services (AWS) essentials and architecting for Data Analytics (Part 1). Enroll here. 
• 04/09/18: Amazon Web Services (AWS) essentials and architecting for Data Analytics (Part 1). Enroll here. 

Past NLI class:

• 9/18/17: Amazon Web Services (AWS) essentials and architecting for Data Analytics (Part 1). Enroll here.
• 11/20/17: Amazon Web Services (AWS) essentials and architecting for Data Analytics (Part 1). Enroll here.  

This course builds on the earlier course "Amazon Web Services‎ (AWS) essentials and architecting for Data Analytics." This course provides an overview of Amazon Web Services (AWS) services that can help you perform data analytics for your research in the AWS ecosystem. The course instructor who has received AWS Instructor-led training and AWS certifications will teach you how to use advanced AWS services, such as Amazon EMR, Amazon Redshift, and Amazon Kinesis. The course includes a combination of lectures and hands-on labs that expose students to these techniques.

You will learn about creating big data environments, choosing appropriate AWS data storage options, working with Amazon RDS, Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, etc. and leveraging best practices to architect workflows to process research dataset and retrieve results in a cost-effectiveness way.

This course is intended for faculty and students who are interested in learning about the advanced AWS services for performing data analytics in the cloud environment.

We recommend that attendees of this course have the following prerequisites:

1. Attend NLI course - “Amazon Web Services‎ (AWS) essentials and architecting for Data Analytics”
2. Working knowledge of core AWS services and implementation.

Note:
You must bring your own laptop device to class in order to run hands-on labs. 
Attendees will receive QWIKLABS (https://qwiklabs.com) credits after completing the course.

Current NLI class:

• 02/20/18: Data Analytics using Amazon Web Services (AWS) (Part 2). Enroll here.
• 04/10/18: Data Analytics using Amazon Web Services (AWS) (Part 2). Enroll here.

Past NLI class:

• 9/19/17: Data Analytics using Amazon Web Services (AWS) (Part 2). Enroll here.
• 11/21/17: Data Analytics using Amazon Web Services (AWS) (Part 2). Enroll here.