AWS Data Querying with S3 & Athena

  • BeginnerLevel

  • 111+Students Enrolled

  • 2 Hrs Duration

  • 4.8Average Rating

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About this Course

  • Build a strong AWS data foundation by learning storage types, data stores, and how they support real analytics pipelines.
  • Organize datasets in Amazon S3 using practical conventions (buckets, prefixes, partition-friendly layouts) for clean retrieval.
  • Apply governance controls such as bucket policies, public access blocks, and versioning to keep data secure and auditable.
  • Query S3 data using Athena and automate schema discovery with Glue so your lake stays queryable as data grows.
  • Understand when to use RDS vs Redshift and learn how to load data from S3 into Redshift for structured analytics.

Course Benefits

  • Understand AWS storage types and choose the right store for each data workload.
  • Build clean, scalable S3 layouts that stay queryable and easy to maintain.
  • Implement S3 governance patterns used in real production environments.
  • Query S3 data with Athena and automate schemas using Glue crawlers and Python.
  • Gain a clear mental model of RDS vs Redshift and how S3 feeds warehouse analytics.

Learning Outcomes

AWS Storage Basics

Object vs file vs block and when to use each of them

S3 Setup & Access

Buckets, prefixes, versioning, policies, permissions

Cost & Retention Rules

Lifecycle tiering, retention and automated cleanup.

Glue Schema Automation

Automate Crawlers + Python to infer and manage schemas.

Who Should Enroll

  • Data analysts, data engineers, and data scientists who want a practical AWS data foundation.
  • Professionals moving data workloads to S3 and needing governance, retention, and cost control basics.
  • Learners who want hands-on experience with Athena SQL, Glue automation, and Redshift ingestion.

Course Curriculum

Start with AWS storage and data store fundamentals, then build strong S3 organization and governance. Next, query data on S3 using Athena and automate schemas using Glue + Python. Finish by comparing RDS vs Redshift.

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Get clarity on the core storage building blocks in AWS: object, file, and block storage. You will also understand how S3 fits into the broader AWS data ecosystem and when to use S3 vs databases for different analytics needs.

  1. 1. AWS Storage Fundamentals: Object, File, and Block Storage

  2. 2. AWS Data Stores Overview: S3, Databases, and Use Cases

Learn how to structure data properly in S3 using buckets, prefixes, and clean organization patterns that scale. Then apply real governance controls like versioning, bucket policies, and public access settings to keep data secure and production-ready.

  1. 1. Amazon S3 Foundations: Buckets, Prefixes, and Data Organization

  2. 2. S3 Governance: Versioning, Bucket Policies, and Public Access

  3. 3. S3 Lifecycle Management: Retention, Tiering, and Automation

Turn S3 into a queryable data lake by running SQL directly on stored files using Amazon Athena. You will also automate schema discovery and table creation using AWS Glue Crawlers and Python so your data remains query-ready as it grows.

  1. 1. Amazon Athena Basics: Querying S3 with SQL

  2. 2. Hands-On with Athena: Querying CSV Data Stored in S3

  3. 3. AWS Glue Crawlers: Automating Schema Discovery for Athena

  4. 4. Python Automation for Athena: Programmatic Table Schema Generation

Understand where structured storage fits in analytics by comparing RDS and Redshift in practical terms. Then learn how to load data from S3 into Redshift to support warehouse-style querying and reporting workflows.

  1. 1. Structured Storage on AWS: RDS vs Redshift + RDS Setup Walkthrough

  2. 2. Amazon Redshift Data Loading: Ingesting Data from S3

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Jatin Goel

DevOps Mentor

Jain Goel is a Cloud Architect, DevOps Engineer, and Data Analyst with 10+ years of experience. A freelance DevOps mentor, he shares practical learnings and is a results-driven Project Lead and technical trainer with strong leadership.

Get this Course Now

With this course you’ll get

  • 2 Hours

    Duration

  • Jatin Goel

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

You will learn AWS storage fundamentals, S3 organization and governance, Athena SQL querying, Glue-based schema automation, and Redshift loading from S3.

It is beginner-friendly for AWS concepts, but ideal if you have basic data or SQL familiarity for the Athena modules.

It is hands-on and workflow-driven, including Athena querying and schema automation steps you can reuse.

Basic SQL helps. Python is introduced for automation in a guided, practical way.

The course is designed to be completed in about 2 hours 26 minutes.

Yes. The course focuses on how S3, Athena, Glue, RDS, and Redshift work together in real analytics workflows.

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