Serverless Analytics on AWS
AWS Glue and Amazon Athena have transformed the way big data workflows are built in the day of AI and ML. Learn how to build for now and the future, how to future-proof your data, and know the significance of what you’ll learn can't be overstated.
What you'll learn
How to architect and build big data analytics in the AWS cloud in the day of AI and ML has been transformed by both AWS Glue and Amazon Athena. In this course, Serverless Analytics on AWS, you'll gain the ability to have one centralized data source for all your globally scattered data silos regardless if the data is structured, unstructured, or semi-structured so you can perform multiple types of advanced analytics on the data by multiple people simultaneously without affecting the underlying data store wherever in the world each data set is located, keeping the data in sync with any changes to the source data. First, you'll learn how to use AWS Glue Crawlers, AWS Glue Data Catalog, and AWS Glue Jobs to dramatically reduce data preparation time, doing ETL “on the fly”. Next, you’ll discover how to immediately analyze your data without regard to data format, giving actionable insights within seconds. Finally, you’ll explore how to use AWS best practices to keep up by having AI and ML analytics incorporated into your analytics workflows, future-proofing your data via immutable logs. When you’re finished with this course, you'll have the skills and knowledge of using state of the art serverless technologies to provide a myriad of insight types whenever you need them.
Table of contents
- Amazing Amazon S3 Data Lake Framework for Multi-analysis 6m
- Amazon Aurora: The Relational Database of Choice for Analytics 5m
- AWS Glue: What It Is, What It Does, and the Business Problems It Solves 7m
- Amazon Athena: What It Is, What It Does, and the Business Problems It Solves 4m
- Scenario: Globomantics - An NBA Trivia Provider 3m
- Module Demo Objectives 3m
- Demo: Configuring an Amazon S3 Bucket and Uploading the Python Transformation Script 2m
- Python Transformation Script Walkthrough 2m
- Demo: Creating the AWS Glue Infrastructure Architecture 6m
- Review of GlueStack’s Resources, Outputs, and Parameters, and Previewing What’s to Come in the Next Demo 3m
- Demo: Running the First Crawler to Populate the Glue Data Catalog and Run the Glue ETL Job to Transform the Data 6m
- Review the Results of Running the AWS Glue Crawler and the AWS Glue Job 2m
- Demo: Creating a New AWS Glue Crawler to Crawl the Parquet-formatted Data 3m
- Demo Review: Creating New AWS Crawler and Crawling the Parquet-formatted Data 11m
- Amazon Athena’s Benefits and Using Parquet-formatted Data with Amazon Athena 4m
- Amazon Athena’s Dual Data Catalogs 2m
- Amazon Athena’s SerDes, Data Types, and Compression Formats 3m
- Databases and Tables in Amazon Athena 5m
- Demo: Amazon Athena Databases and Tables 9m
- Using Partitions in Amazon Athena 3m
- Monitoring and Tracing Ephemeral Data 4m
About the author
Kim Schmidt has been working in the technology industry for more than 12 years with an extremely broad array of titles and using very diverse technologies. She holds many industry certifications and awards. Companies Kim has worked for or with include Microsoft, Dun & Bradstreet, Google, Amazon Web Services, and a couple of Augmented Reality companies. Kim is the Founder and CEO of DataLeader, an AWS Partner and Vendor company. DataLeader focuses on AWS Big Data Architecture, Data Solutions,... more