DEV Community: ADHIYA R

AWS Cloud Workshop – Session 2 ☁️

ADHIYA R — Wed, 16 Sep 2026 04:04:03 +0000

The second half of the AWS Cloud Workshop was a hands-on session where I explored three important AWS services — IAM, Amazon CloudFront, and AWS Lambda.

🔐 IAM – Identity and Access Management

The first task I worked on was creating an IAM user in AWS.

I created the user, configured the required permissions, and logged in using the IAM user credentials to verify the access.

This helped me understand how AWS IAM can be used to manage users and control access to AWS resources.

🌐 Amazon CloudFront

Next, I explored Amazon CloudFront, AWS's content delivery network (CDN).

I learned how CloudFront helps deliver web content efficiently by using a distributed network of edge locations, allowing content to be served closer to users.

As part of the workshop, I also worked through the CloudFront setup and understood the steps involved in configuring a distribution.

⚡ AWS Lambda

The final task was working with AWS Lambda.

I created a Lambda function and configured an Amazon S3 event trigger. I then tested the setup and verified that the Lambda function was triggered successfully.

This gave me a practical understanding of serverless computing and how AWS Lambda can respond automatically to events.

💡 What I Learned

This session gave me practical exposure to:

AWS IAM and user access management
Amazon CloudFront and content delivery
AWS Lambda and serverless computing
S3 event triggers
Connecting different AWS services together

📌 Conclusion

The second-half session was a valuable hands-on experience with AWS. Working with IAM, CloudFront, and Lambda helped me understand how different AWS services can be configured and used together in cloud-based applications.

Looking forward to exploring more AWS services and building more practical projects in the upcoming sessions! 🚀

Adhiya Meets Athena: Exploring Amazon Athena 🔍☁️

ADHIYA R — Wed, 16 Sep 2026 01:55:04 +0000

Introduction:

As students, we work with data almost every day. Whether it is a machine learning dataset, college records, project logs, or survey results, analyzing large amounts of data can become difficult when everything has to be downloaded and processed locally.

While learning AWS, I came across Amazon Athena, a service that makes this process much simpler.

Amazon Athena is a serverless interactive query service that allows us to analyze data stored in Amazon S3 using standard SQL. The interesting part is that we do not need to set up or manage servers, clusters, or other infrastructure before running queries.

In this blog, I will explain Amazon Athena in simple terms, how it works, its important features, a practical student use case, and some things to consider before using it.

What is Amazon Athena?

Amazon Athena is a serverless analytics service from AWS.

Its main purpose is to allow users to query data directly where it is stored, particularly in Amazon S3, using SQL.

For example, imagine that I have a large CSV file containing student project information:

student_id,department,project,score
101,AI&ML,RenalScan,92
102,CSE,SmartCampus,87
103,AI&ML,ChurnGuard,90

Instead of downloading the entire dataset and processing it on my laptop, I can store the file in S3 and use Athena to query it.

For example:

sql
SELECT department, AVG(score)
FROM student_projects
GROUP BY department;

Athena processes the query and returns the result.

It also supports multiple common data formats, including CSV, JSON, ORC, Avro, and Apache Parquet.

Why Was Amazon Athena Created?

Analyzing large datasets traditionally required infrastructure such as servers, database systems, or data-processing clusters.

For students and smaller teams, setting up and maintaining this infrastructure can be unnecessary for many analytics tasks.

Amazon Athena provides a serverless approach. We can keep data in Amazon S3, define its schema, and start running SQL queries without managing the underlying servers.

This makes Athena particularly useful for ad-hoc analysis, log analysis, data exploration, and querying data lakes.

The main idea is simple:

Store the data in the cloud and query it when you need it.

How Does Amazon Athena Work?

The basic workflow looks like this:

The process can be explained in a few steps:

Store the dataset in an Amazon S3 bucket.
Open Amazon Athena.
Define the structure/schema of the data.
Write a SQL query.
Athena reads the required data from S3.
The query is processed without the user managing servers.
Results are displayed and can also be stored in S3.

Athena is designed to execute queries in parallel and automatically scale the query-processing infrastructure.

Key Features of Amazon Athena

1.Serverless Architecture

The biggest feature of Athena is that it is serverless.

There is no need to create or maintain EC2 instances, configure clusters, install database software, or manually handle scaling.

AWS manages the infrastructure required to execute the queries, allowing the user to focus mainly on the data and SQL.

For a student, this is useful because we can experiment with analytics without first learning how to manage a complete data-processing cluster.

2.Query Data Using Standard SQL

Athena allows users to analyze data using SQL.

For someone who already knows SQL through a DBMS course, this makes Athena relatively approachable.

For example:

sql
SELECT project, AVG(score) AS average_score
FROM student_projects
GROUP BY project
ORDER BY average_score DESC;

Athena supports SQL operations such as joins, window functions, and arrays, and supports several data formats including CSV, JSON, ORC, Avro, and Parquet.

3.Integration with Amazon S3

Athena works directly with data stored in Amazon S3.

We do not have to first move the entire dataset into a traditional database before querying it.

This makes the combination of S3 + Athena useful for building a simple data lake architecture.

Athena can also use the AWS Glue Data Catalog to help define and manage table and partition metadata.

4.Workgroups for Managing Queries

Athena provides workgroups that can be used to separate workloads and teams.

For example, a college could create separate workgroups for:

Workgroups can also be used to control access, configure query-result locations, monitor usage, and establish data-usage limits.

College / Student Use Case 🎓

A practical use case for my college would be a Student Project Analytics System.

Suppose a department has thousands of project records containing:

Student ID
Department
Project title
Technology used
Project score
Academic year
Project category

These records could be stored in S3.

Athena could then be used to answer questions such as:

sql
SELECT technology, COUNT(*) AS project_count
FROM student_projects
GROUP BY technology
ORDER BY project_count DESC;

The department could use the results to understand which technologies students are using most frequently.

Another query could calculate the average project score:

sql
SELECT department, AVG(score) AS average_score
FROM student_projects
GROUP BY department;

This would allow faculty members to analyze project trends without building and maintaining a dedicated database server for every analysis task.

A Simple Practical Example

Imagine that I have uploaded a CSV file called:
student_projects.csv
to an S3 bucket.

After defining the table structure in Athena, I can run:

sql
SELECT *
FROM student_projects
LIMIT 10;

To find the number of projects in each department:

sql
SELECT department, COUNT(*) AS total_projects
FROM student_projects
GROUP BY department;

To find high-scoring projects:

sql
SELECT student_id, project, score
FROM student_projects
WHERE score >= 90
ORDER BY score DESC;

The important point is that these queries can be run against data stored in S3 rather than requiring me to download the entire dataset onto my computer.

Athena can be accessed through the AWS Management Console, API, AWS CLI, SDKs, and supported JDBC/ODBC connections.

Advantages of Amazon Athena:

No Server Management

Athena is serverless, so users do not need to manage servers, clusters, software updates, or infrastructure scaling.

Easy for SQL Users

Anyone familiar with SQL can start exploring datasets without learning an entirely new query language.

Scalable

Athena is designed to automatically scale query execution and process queries in parallel.

Works with Large Datasets

Athena can be used for interactive analysis of large datasets stored in S3.

Integration with AWS

It works with services and tools such as Amazon S3, AWS Glue Data Catalog, IAM, CloudWatch, and business intelligence tools.

Limitations and Things to Consider:

Cost

Athena is not simply a completely free service.

For SQL queries, the default pricing model is based on the amount of data scanned. AWS also provides capacity-based pricing for certain workloads.

This means poorly optimized queries over very large datasets can become expensive.

Using compression, partitioning, and columnar formats such as Parquet can reduce the amount of data scanned and therefore reduce query costs.

Complexity

Basic queries are easy, but working with large datasets requires knowledge of data formats, schemas, partitions, permissions, and query optimization.

Scalability

Athena itself scales automatically, but the way data is stored still matters. Poorly organized datasets can result in unnecessary data scanning and slower or more expensive queries.

Security

Data access needs to be configured carefully. Athena works with AWS IAM policies and Amazon S3 bucket policies to control who can access the underlying data.

Athena can also query encrypted data stored in S3 and can encrypt query results.

Conclusion:

Amazon Athena is a useful AWS service for anyone who needs to analyze large amounts of data without managing servers.

Its combination of serverless architecture, SQL support, S3 integration, scalability, and flexible query management makes it useful for students as well as organizations.

For a student like me, Athena is especially interesting because it connects concepts that we already learn in college—such as SQL, databases, data analytics, and cloud computing.

A simple combination of Amazon S3 + Amazon Athena can turn a collection of raw datasets into something that can be queried and analyzed using familiar SQL commands.

My biggest takeaway from learning about Athena is that cloud computing is not always about managing more infrastructure. Sometimes, the advantage is that AWS manages the infrastructure so we can concentrate on solving the actual data problem. 🚀

References

My First AWS S3 Hands-On Experience ☁️

ADHIYA R — Tue, 15 Sep 2026 06:58:24 +0000

As part of my learning journey in Artificial Intelligence and Machine Learning, I recently attended the first session of a hands-on Cloud Computing Workshop.

Since cloud platforms are becoming an important part of deploying and scaling applications, especially in AI/ML, I wanted to get more comfortable working with cloud services rather than limiting my learning to theory.

For the first session, we worked with Amazon S3 (Simple Storage Service) and went through the basic process of creating a bucket, configuring it, uploading an object, and generating a URL to access the object.

🪣 Creating an S3 Bucket

We started with the basic building block of S3 — a bucket.

I created a bucket in the US East (N. Virginia) – us-east-1 region and selected the General purpose bucket type.

While creating the bucket, I got to explore some of the configurations available in S3, including:

Object Ownership
Block Public Access
Bucket Versioning
Tags
Default Encryption

These settings made me realize that creating cloud storage isn't simply about choosing a name and uploading files. There are also important considerations around ownership, access, security, and data management.

The workshop screenshots show the different configurations I went through while creating the bucket.

📤 Uploading an Object

Once the bucket was created, the next step was to upload an object into it.

The upload was completed successfully, and I could see the object listed inside the bucket.

This gave me a clearer understanding of the basic S3 structure:

S3 → Bucket → Object

Instead of thinking of S3 simply as online file storage, I started understanding how AWS organizes and manages objects within buckets.

💻 Uploading My Portfolio

As part of the hands-on activity, I also worked with my personal portfolio website.

I prepared my portfolio as an index.html file and uploaded it to the S3 bucket as an object. This was a simple but useful way to understand how a web file can be stored and accessed using cloud storage.

Instead of just uploading a random file, using my own portfolio made the activity more practical for me because it connected the AWS concepts with something I can actually use for my projects and future work.

This also helped me understand the basic flow of working with a web file in the cloud:

Portfolio → index.html → S3 Bucket → Object URL

🔗 Generating a Predefined URL

The next step was generating a predefined URL for the uploaded object.

The S3 console provides an option to share an object using a predefined URL with a specified validity period. This was an interesting part of the session because it introduced the idea of providing access to a stored object for a limited period rather than treating the entire bucket as publicly accessible.

The screenshot from the session shows the predefined URL configuration and the option to set its validity period.

💡 What I Took Away From the Session

Although this was an introductory S3 exercise, it helped me connect some of the cloud concepts I have studied with an actual AWS environment.

The main things I worked with were:

☁️ Cloud Storage — understanding how S3 stores objects in buckets.

🪣 Buckets & Objects — learning the basic structure used by S3.

🔐 Access & Security — exploring settings such as Block Public Access and Object Ownership.

🔒 Data Protection — getting introduced to default encryption.

🔗 Controlled Access — generating a predefined URL for an uploaded object.

As an AI/ML student, I think understanding these cloud fundamentals is important because building an ML model is only one part of the process. Eventually, models and applications need to store data, access resources, and be deployed in real environments.

🚀 Looking Forward

This was just the first session, but it gave me a good starting point for working with AWS.

I'm looking forward to exploring more cloud services in the upcoming sessions and understanding how these services can fit into real-world AI/ML applications.

One session done, more cloud concepts to explore! ☁️