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AWS Analytics Services - Hands-On Lab Demos
Rating: 4.5 out of 5(5 ratings)
4,798 students

AWS Analytics Services - Hands-On Lab Demos

Learn AWS Analytics with hands-on demos of Athena, Glue, EMR, Redshift, OpenSearch, and QuickSight.
Created byDeepak Dubey
Last updated 10/2025
English
English [Auto],

What you'll learn

  • Use AWS Glue crawlers to build a Data Catalog and run serverless ETL (Extract, Transform, Load) jobs.
  • Run serverless SQL queries directly on data in S3 using Amazon Athena.
  • Build and share interactive Business Intelligence (BI) dashboards from your data using Amazon QuickSight.
  • Deploy and query a high-performance data warehouse using Amazon Redshift.

Explore related topics

Course content

1 section • 10 lectures • 3h 11m total length

  • Amazon Athena - Hands-On Demo11:40

    Set up an S3 bucket for Athena query results, configure the query result location, and create a dedicated AWS data catalog database with an external table for log data.

  • AWS Data Exchange - Hands-On Demo12:28

    Explore the AWS Data Exchange catalog, subscribe to datasets, download and store covid data in an S3 bucket, analyze with Athena, and clean up resources.

  • Amazon EMR - Hands-On Demo18:20

    learn to set up an s3 input/output bucket, launch a single EC2 instance EMR cluster, deploy and run a spark job, monitor progress, view results, and clean up resources.

  • AWS Glue - Hands-On Demo28:31

    Learn end-to-end AWS analytics with glue: create S3 buckets, build a data catalog and crawler, run a python ETL job, and query results with Athena.

  • Amazon Kinesis Data Streams - Hands-On Demo14:29

    Join a hands-on demo of Amazon Kinesis Data Streams, creating a demo stream, generating sample data with a Lambda function, processing it via another Lambda, and storing results in DynamoDB.

  • AWS Lake Formation - Hands-On Demo28:44

    Learn to set up AWS Lake Formation data lake: create an S3 bucket, register lake location, build Glue crawler and database, configure permissions, query with Athena, and clean up.

  • Amazon Managed Streaming for Apache Kafka (Amazon MSK) - Hands-On Demo24:08

    Follow an end-to-end hands-on demo of Amazon MSK: set up a cluster, IAM policy and role, an EC2 client, create a Kafka topic, and produce and consume messages.

  • Amazon OpenSearch Service - Hands-On Demo18:23

    Explore Amazon OpenSearch Service through a hands-on demo that creates a domain, configures access, ingests sample log data, builds index patterns and visualizations, runs basic searches, and cleans up.

  • Amazon QuickSight - Hands-On Demo13:13

    Explore Amazon QuickSight by signing up in the AWS console, connect data sources, visualize with Spice, create charts and dashboards, publish and share insights.

  • Amazon Redshift - Hands-On Demo21:58

    Perform a hands-on Amazon Redshift demo: build a cluster, configure security, load data from S3 with the copy command, query via the editor, analyze performance, and clean up.

Requirements

  • An active AWS account (Free Tier recommended) is essential to follow along with the hands-on demos.
  • A basic familiarity with data concepts (like what a database is, what SQL stands for, and the idea of ETL) is highly recommended.

Description

Stop reading dry documentation and start seeing AWS Analytics services in action!

The AWS Analytics ecosystem is incredibly powerful, but learning it from theory alone is difficult. To truly understand how to build a data pipeline, you need to see these services working together.

This course is built on one simple principle: learn by watching practical, hands-on demos.

We skip the long, boring theory slides and get straight into the AWS console. This course is a comprehensive collection of over-the-shoulder lab demonstrations where I walk you through the setup, configuration, and real-world use of the most important AWS Analytics services. You will see how to build, why we're clicking each button, and what the end result looks like.

This is not a "what is" course; this is a "how-to" course.

Join me as we build, configure, and run end-to-end analytics solutions. You will get detailed, practical demos of:

  • Amazon Athena: Running serverless SQL queries directly on your S3 data.

  • AWS Glue: Using crawlers to build a Data Catalog and running serverless ETL (Extract, Transform, Load) jobs.

  • Amazon EMR: Launching and managing a big data cluster (like Spark) to process massive datasets.

  • Amazon Redshift: Setting up a high-performance data warehouse and running complex analytical queries.

  • Amazon QuickSight: Building and sharing interactive Business Intelligence (BI) dashboards to visualize your data.

  • Amazon OpenSearch Service: Deploying a cluster for powerful log analytics, monitoring, and real-time search.

  • Amazon MSK (Managed Kafka) & Kinesis: Understanding and configuring services for real-time data streaming.

  • AWS Lake Formation: Building, securing, and managing a data lake in a matter of minutes.

  • ...and more!

Who is this course for?

  • Data Engineers and Data Analysts who want to learn to build pipelines on AWS.

  • Solutions Architects who need to design modern data analytics solutions.

  • Developers who need to integrate data streaming or analytics into their applications.

  • Individuals preparing for the AWS Certified Data Analytics - Specialty exam.

  • Any tech professional who learns best by "seeing" and "doing" rather than just reading slides.

If you're ready to gain practical, hands-on confidence in the AWS Analytics stack, this course is for you.

Enroll today and let's start building!

Who this course is for:

  • Developers who need to build data processing pipelines or analytics features into their applications.

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