What you'll learn

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

Requirements

Basic knowledge of AWS is useful but not mandatory

Description

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 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 sennt analysis, and store it in the data lake for querying.

I look forward to meeting you soon!

Thank you!

Who this course is for:

Decision Makers who want to understand data lake

Professionals who work with structured and unstructured data



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