AWS Encryption for Data Analytics

Intermediate
3m 7s
3,904
4.5/5

The use of Big Data is becoming commonplace within many organizations that are using Big Data solutions to perform large scale queried data analysis with business intelligence toolsets to gain a deeper understanding of data gathered.

Within AWS, this data can be stored, distributed and consumed by various different services, many of which can provide features ideal for Big Data analysis. Typically, these huge data sets often include sensitive information, such as customer details or financial information.

With this in mind, security surrounding this data is of utmost importance, and where sensitive information exists, encryption should be applied against the data.

This course firstly provides an explanation of data encryption and the differences between symmetric and asymmetric cryptography. This provides a good introduction before understanding how AWS implements different encryption mechanisms for many of the services that can be used for Big Data. These services include:

  • Amazon S3
  • Amazon Athena
  • Amazon Elastic MapReduce (EMR)
  • Amazon Relational Database Service (RDS)
  • Amazon Kinesis Firehose
  • Amazon Kinesis Streams
  • Amazon Redshift

The course covers encryptions options for data when it is at both at-rest and in-transit and contains for the following lectures:

  • Introduction: This lecture introduces the course objectives, topics covered and the instructor
  • Overview of Encryption: This lecture explains data encryption and when and why you may need to implement data encryption
  • Amazon S3 and Amazon Athena Encryption: This lecture dives into the different encryption mechanisms of S3, from both a server-side and client-side perspective. It also looks at how Amazon Athena can analyze data sets stored on S3 with encryption
  • Elastic MapReduce (EMR) Encryption: This lecture focuses on the different methods of encryption when utilizing EMR in conjunction such as EBS and S3. It also looks at application-specific options with Hadoop, Presto, Tez, and Spark
  • Relational Database Service (RDS) Encryption: This lecture looks at the encryption within RDS, focusing on its built-in encryption plus Oracle and SQL Server Transparent Data Encryption (TDE) encryption
  • Amazon Kinesis Encryption: This lecture looks at both Kinesis Firehose and Kinesis Streams and analyses the encryption of both services.
  • Amazon Redshift Encryption: This lecture explains the 4 tiered encryption structure when working with Redshift and KMS. It also explains how to encrypt when working with CloudHSM with Redshift.
  • Summary: This lecture highlights the key points from the previous lectures

Resources mentioned throughout this course

Cloud Academy Courses:

AWS Resources:

 

About the Author
Students
234,932
Labs
1
Courses
232
Learning paths
208

Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.

To date, Stuart has created 250+ courses relating to cloud computing reaching over 1 million+ students.

Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.

He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.

Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.

Covered Topics