How I Used AWS Glue and Athena for Serverless Data Analytics

Nidhi Thakore — Mon, 06 Oct 2025 08:33:28 +0000

As someone who loves building data pipelines, I’ve always been fascinated by how serverless architectures simplify analytics.

Recently, I worked on a project where I built a fully serverless data analytics pipeline using AWS Glue and Amazon Athena — no servers, no EC2, no clusters, and no headaches.

In this blog, I’ll take you through how I used these two AWS powerhouses to go from raw S3 data → cleaned data → analytical insights — all without managing a single server.

Step 1: Store Raw Data in Amazon S3
I started with raw e-commerce transaction data — product sales, customers, and timestamps — stored in S3.
my-ecommerce-analytics/
raw/
sales01.csv
sales02.csv
customers.csv
transformed/

Step 2: Crawling Data Using AWS Glue
Next, I created an AWS Glue Crawler and pointed it to my s3 bucket.

What’s amazing about Glue Crawlers is that they automatically detect schema and data types and create tables inside the AWS Glue Data Catalog.

After running the crawler, I had:
sales_data
customers_data
in a database called ecommerce_analytics

You can also schedule crawlers to run daily or hourly — perfect for continuously updated S3 data.

Step 3: Exploring Data with Amazon Athena
With the Glue Catalog ready, I moved to Amazon Athena that allows you to run SQL queries directly on S3 data, without having to load it into a database where I explored my sales data, aggregated revenue numbers, and filtered out any invalid or duplicate records.

You can write your own queries to perform these operations — it feels just like using a normal SQL database.

Step 4: Transforming Data with AWS Glue Jobs
Raw data is rarely perfect, so I used AWS Glue ETL Jobs to clean and transform it where i created a Glue job in Python to remove duplicates, standardize timestamp formats, and join sales data with customer information.

Once transformed, I stored the cleaned data back in S3 — this time in Parquet format to make future queries faster and more cost-efficient.

If you’re implementing this, you can write your own ETL logic inside Glue Studio or the Glue Job editor.

Step 5: Querying Transformed Data with Athena
After transformation, I returned to Athena to query the cleaned data. This is where you can perform your own analytical queries like finding top-selling products, analyzing sales patterns, or identifying high-value customers.

Athena makes it effortless — just write standard SQL queries, and it processes everything directly from S3

If you’re exploring AWS as a student, data engineer, or cloud enthusiast, I highly recommend trying this out with your own dataset so that You can understand the true power of serverless analytics once you query data sitting in S3 — in seconds — without spinning up a single machine.

Event-Driven Architectures on AWS: Beyond Lambda

Nidhi Thakore — Fri, 05 Sep 2025 08:45:10 +0000

When most people hear event-driven architecture on AWS, they instantly think Lambda.
And yes, Lambda is amazing — serverless, pay-per-use, and perfect for quick triggers.

But here’s the catch → event-driven systems are much bigger than just Lambda.

But the Question might arise in your mind
Why Event-Driven?

Traditional architectures rely on polling or batch jobs. Event-driven systems flip the script:-
-> You don’t ask if something happened.
-> You react instantly when it does.
This makes applications faster, cheaper, and more resilient.

AWS Building Blocks for Event-Driven Systems

Think of AWS event-driven architecture as a team of specialists, each with a unique role:

EventBridge → The Traffic Controller
Decides where the event should go. Perfect for connecting apps, services, and even third-party SaaS without tight coupling.

SNS (Simple Notification Service) → The Broadcaster
Shouts the event out to many listeners at once — email, SMS,
Lambda, or other apps. Great for fan-out patterns.

SQS (Simple Queue Service) → The Reliable Mailbox
Holds events safely until someone is ready to process them. Ensures nothing gets lost, even during traffic spikes.

Step Functions → The Workflow Manager
Coordinates multi-step processes. Adds retries, error handling, and parallel execution to keep business workflows smooth.

Lambda → The Quick Responder
Executes business logic instantly. Serverless, auto-scaling, and cost-effective — but just one piece of the puzzle.

Event-driven architectures are no longer “nice to have” — they’re becoming the default way to design modern applications. Businesses want systems that are:

Real-time → responding instantly to customer actions

Scalable → handling unpredictable workloads with ease

Cost-efficient → paying only when something actually happens

Resilient → loosely coupled so failures don’t cascade

AWS gives us the perfect toolkit to achieve this: EventBridge, SNS, SQS, Step Functions, and Lambda — each playing a distinct role but working together seamlessly.

The real shift for engineers and architects is moving away from thinking in terms of servers and cron jobs to thinking in terms of events and reactions.

And with AWS, event-driven design means building apps that don’t just exist in the cloud — they listen, react, and scale with the world around them.

Forem: Nidhi Thakore

How I Used AWS Glue and Athena for Serverless Data Analytics

Event-Driven Architectures on AWS: Beyond Lambda

AWS Building Blocks for Event-Driven Systems