Building a Serverless Data Analytics Pipeline with Amazon S3, AWS Glue, and Amazon Athena
Learn how Amazon S3, AWS Glue, and Amazon Athena can work together to build a simple serverless data analytics pipeline. This beginner-friendly guide explains the architecture, workflow, SQL analysis, and practical best practices.
Building a Serverless Data Analytics Pipeline with Amazon S3, AWS Glue, and Amazon Athena
Introduction
Data is generated everywhere — websites, mobile applications, businesses, IoT devices, and online services. As the amount of data increases, organizations need a simple and scalable way to store and analyze it.
AWS provides several services that can work together to build a serverless data analytics pipeline. In this article, we will look at how Amazon S3, AWS Glue, and Amazon Athena can be combined to create a simple analytics workflow.
The best part is that we do not need to manage servers or manually configure a database server.
The Architecture
A simple data analytics pipeline can look like this:
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Data Source
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Amazon S3
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AWS Glue Crawler
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Glue Data Catalog
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Amazon Athena
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SQL AnalysisEach AWS service has a specific responsibility.
1. Amazon S3 — Store the Data
Amazon Simple Storage Service (Amazon S3) can be used as the storage layer.
For example, imagine that we have a CSV file containing sales information:
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order_id,product,category,price,quantity
101,Laptop,Electronics,60000,2
102,Mouse,Accessories,800,5
103,Keyboard,Accessories,1500,3Instead of storing this data on a local computer, we can upload it to an S3 bucket.
S3 provides highly scalable object storage and can act as the foundation of our data lake.
2. AWS Glue — Discover the Data
After uploading the data to S3, we need to understand its structure.
This is where AWS Glue becomes useful.
AWS Glue is a serverless data integration service that can discover, prepare, transform, and catalog data. A Glue crawler can inspect data stored in S3 and automatically infer information such as:
- Column names
- Data types
- Table structure
- Data location
The discovered information is stored in the AWS Glue Data Catalog.
This means we do not have to manually describe every dataset before analyzing it.
3. AWS Glue Data Catalog
The Glue Data Catalog acts as a central metadata repository.
For example, our CSV file might be represented as a table:
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sales
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order_id
product
category
price
quantityThe actual data remains in Amazon S3, while the Data Catalog stores information about how that data should be interpreted.
This allows other AWS analytics services to understand the dataset.
4. Amazon Athena — Query the Data
Now comes the interesting part.
Amazon Athena allows us to analyze data stored in Amazon S3 using standard SQL.
We do not need to create or maintain a database server.
For example, we can calculate the total sales using:
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SELECT
SUM(price * quantity) AS total_sales
FROM sales;We can also find sales by category:
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SELECT
category,
SUM(price * quantity) AS total_sales
FROM sales
GROUP BY category
ORDER BY total_sales DESC;Athena uses the Glue Data Catalog to understand the structure of the data stored in S3.
5. Why Use This Architecture?
This architecture has several advantages.
Serverless
We do not need to manage servers for the storage, cataloging, or SQL querying layers.
Scalable
S3 can store very large amounts of data, while AWS Glue and Athena can process and analyze datasets without requiring us to manually manage infrastructure.
SQL-Based Analysis
Data analysts who already know SQL can use Athena without learning a completely new query language.
Flexible Data Formats
Athena can work with formats such as CSV, JSON, Parquet, and ORC.
Pay-as-you-go
Serverless services allow us to avoid maintaining infrastructure that sits idle.
6. A Simple Real-World Example
Imagine an e-commerce company receiving thousands of orders every day.
The company could follow this workflow:
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Customer Orders
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Amazon S3
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AWS Glue
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Glue Data Catalog
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Amazon Athena
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Sales AnalysisThe data team could then ask questions such as:
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SELECT category, COUNT(*)
FROM sales
GROUP BY category;Or:
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SELECT product, SUM(quantity)
FROM sales
GROUP BY product
ORDER BY SUM(quantity) DESC;This allows the team to quickly explore business data without maintaining a traditional analytics server.
7. Important Best Practices
When building a real-world pipeline, a few things should be considered.
Organize S3 Data Properly
Instead of putting everything into one location, organize data using meaningful prefixes such as:
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sales/year=2026/month=10/day=08/This can make data management and querying more efficient.
Use Efficient File Formats
For large datasets, columnar formats such as Apache Parquet can be more efficient than repeatedly querying large CSV files.
Control Permissions
Use AWS Identity and Access Management (IAM) to control which users and services can access your data.
Monitor Costs
Athena charges based on the amount of data scanned by queries, so reducing unnecessary data scanning can help control costs.
8. What I Learned
The main lesson from this architecture is that building a data analytics system does not always require managing servers.
With:
- Amazon S3 → Store data
- AWS Glue → Discover and prepare data
- Glue Data Catalog → Store metadata
- Amazon Athena → Query data using SQL
we can create a simple and scalable analytics workflow.
This approach is especially useful for data analysts, data scientists, and developers who want to focus on working with data instead of maintaining infrastructure.
Conclusion
AWS provides powerful building blocks for modern data analytics.
A combination of Amazon S3, AWS Glue, and Amazon Athena provides a straightforward way to create a serverless analytics pipeline. Data can be stored in S3, discovered and cataloged using Glue, and analyzed directly with SQL through Athena.
For someone starting their journey in cloud and data science, this architecture is a great example of how cloud services can work together to solve a real-world data problem.
The key idea is simple: store the data, catalog it, and query it — without managing servers.
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