Data Lake in AWS

Hands on serverless integration experience with Glue, Athena, S3, Kinesis Firehose, Lambda, Comprehend AI

Hello, my name is Chandra Lingam, and I am your instructor for Data Lake in AWS.

What you’ll learn

  • Learn about Data Lake vs. Data Warehouse.
  • Key components of a Data Lake Architecture.
  • Query files directly using SQL.
  • Hands-on integration using Kinesis Firehose, Lambda, Comprehend AI, Glue, Athena and S3.

Course Content

  • Introduction –> 7 lectures • 17min.
  • Querying and Analytics –> 3 lectures • 10min.
  • Lab – AWS Account and User Setup –> 4 lectures • 20min.
  • Monitoring, Optimization and Security –> 10 lectures • 43min.
  • Why Cloud Computing? –> 4 lectures • 12min.
  • Lab – Data Catalog and In-place Querying –> 7 lectures • 21min.
  • Glue Catalog Management and Schema Evolution –> 11 lectures • 48min.
  • Lab – Customer Review Sentiment Analysis –> 8 lectures • 28min.
  • Lab – Serverless Application with Kinesis Firehose, Lambda, Comprehend AI, Glue, –> 4 lectures • 18min.
  • Conclusion –> 1 lecture • 1min.

Data Lake in AWS

Requirements

  • Basic knowledge of AWS is useful but not mandatory.

Hello, my name is Chandra Lingam, and I am your instructor for Data Lake in AWS.

In this course, we will start by understanding when a data lake is the right solution as opposed to a data warehouse.

Throughout the next two hours, you will learn all the components of a data lake.

One of its advantages is the flexibility to directly query files using SQL.

You will start by building a Glue Data catalog and using Athena to query.

Then, we will work on Glue ETL, a powerful Apache Spark-based solution for data transformation.

You will learn finer-points on Glue Catalog Management and Schema Evolution

To demonstrate the scalability of Athena, we will query the Amazon Customer Reviews data set with over 130 million reviews.

Finally, we will build a serverless application using Kinesis Firehose, Lambda, Comprehend AI, Glue, Athena and S3 that can process unlimited customer reviews, perform sentiment analysis, and store it in the data lake for querying.

I look forward to meeting you soon!

Thank you!

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