Learn about best practices, patterns, and tools for designing and implementing data analytics using AWS.
- [Lynn] Hi, and welcome to AWS Analytics. I'm Lynn Langit. In this course we're going to take a look at analytics using AWS services. We're going to start by looking at concepts and patterns, such as understanding batch analytics, streaming analytics, and interactive analytics. Then, we're going to match those patterns to services. We're going to take a look at new services, such as AWS Athena, which allows you to do Sequel quarries on tops of a data lake, and more traditional services like RDS or relational database service, Redshift for data warehousing, DynamoDB for no Sequel, and Kinesis for streaming.
We're then going to look at putting it all together via advanced analytics. Here, we'll understand preparing your data with ETL pipelines or extract, transform, and load. We're going to look at using public data to enhance your analytics, and then build those pipelines. We have lots to work on, so let's get started.
- AWS analytics design concepts
- Files vs. databases
- Which analytics type to use
- Using code tools for analytics
- AWS IoT device message analytics
- Working with data using Spark commands
- Using AWS QuickSight for visualizations
- Top analytics architectural patterns and their associated AWS services
Skill Level Intermediate
Amazon Web Services for Data Sciencewith Lynn Langit3h 56m Intermediate
1. Analytics on AWS
2. Analytic Services
3. AWS Code Tools for Analytics
4. Advanced Analytics
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