This course gives you an overview and hands-on knowledge on how to analyze data, build data-intensive applications and real-time data processing on top of AWS.
After the course, participants will be able to build data products on top of AWS.
You can select any number of modules from the following.
This module makes you familiar and hands-on with Hadoop, Data Warehousing, RDBMS, and NoSQL technologies.
We plan and build an end-to-end data-architecture together. At the end of this module, you will be competent in deciding what technology to use for different use-cases and how these technologies play together in a production setting.
How Amazon Web Services work and how different services fit together.
How to use Athena, an SQL-based service for querying massive amounts of file-based data on S3.
How to use Redshift, a managed Scalable Data Warehouse service, and how to optimize it.
How to create reports and Dashboards with QuickSight, Amazon’s point-and-click BI solution.
Use Real-time SQL based data-analytics with Kinesis Analytics.
SQL knowledge
This module gives you an overview and hands-on knowledge of how cloud-based data warehousing and BI work. You will be hands-on with Amazon’s Data Warehousing, Cloud-based BI, and Real-time analytics tools.
How Amazon Web Services work and how different services fit together.
How to use Athena, an SQL-based service for querying massive amounts of file-based data on S3.
How to use Redshift, a managed Scalable Data Warehouse service, and how to optimize it.
How to create reports and Dashboards with QuickSight, Amazon’s point-and-click BI solution.
Use Real-time SQL based data-analytics with Kinesis Analytics.
SQL knowledge
In this module, you will understand the concepts of real-time data-applications and analytics. You will get hands-on with writing applications that utilize real-time data analytics, reporting, monitoring and alerting.
The module also provides an introduction to serverless real-time services, such as AWS Lambda. At the end of the module, you will be able to design and implement your own streaming data infrastructure.
How to work with scalable Message Queues (SQS)
Message brokers for real-time data processing and analytics (Kinesis)
The ELK Stack:
Elasticsearch – a NoSQL database for real-time analytics
LogStash – real-time data ingestion and distribution
Kibana – Monitoring and visual analytics on real-time data
How to implement Notification and Alerting through SNS
How to utilize AWS Lambda to solve business problems without provisioning and maintaining servers
Intermediate Programming knowledge – preferably Python
In this module, you will learn the machine learning concepts and how to apply them to AWS. You will be hands-on with Amazon’s Artificial Intelligence platform.
How Amazon Web Services work and how different services fit together
How to use the AWS Machine Learning service
Amazon Rekognition: Image and facial analysis
Amazon Polly: text-to-speech service
Amazon Lex: Automatic speech recognition and natural language understanding
Intermediate Programming knowledge – preferably Python