Amazon Web Services: Data Analytics

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  • Course details

    Many modern organizations have a wealth of data that they can draw from to inform their decisions. But all of this information can't truly benefit a business unless the professionals working with that data can efficiently extract meaningful insights from it. Amazon Web Services (AWS) offers data scientists an array of tools and services that they can leverage to analyze data. In this course, learn about best practices, patterns, and tools for designing and implementing data analytics using AWS. Explore key analytics concepts, common methods of approaching analytics challenges, and how to work with services such as Athena, RDS, and QuickSight. Plus, discover how to visualize text-based data in a more visually intuitive way, use partner solutions for analytics from the AWS Marketplace, and more.


    • Click here to view Lynn Langit’s instructor page

      Lynn Langit

      CEO Lynn Langit Consulting LLC

      Lynn Langit is a cloud architect who works with Amazon Web Services and Google Cloud Platform.

      Lynn specializes in big data projects. She has worked with AWS Athena, Aurora, Redshift, Kinesis, and the IoT. She has also done production work with Databricks for Apache Spark and Google Cloud Dataproc, Bigtable, BigQuery, and Cloud Spanner.

      Lynn is also the cofounder of Teaching Kids Programming. She has spoken on data and cloud technologies in North and South America, Europe, Africa, Asia, and Australia.

    Skills covered in this course

  • Welcome

    - [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.

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