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Building a Simple Serverless Data Pipeline with AWS

Building a Simple Serverless Data Pipeline with AWS

Learn how to build a simple serverless data pipeline using Amazon S3, AWS Lambda, AWS Glue, and Amazon Athena. This article explains how data can be stored, processed, transformed, and analyzed using AWS services without managing servers.

K v manoj

Introduction

Data pipelines are an important part of cloud and data engineering. A simple pipeline can collect data, process it, and store the results for further analysis.
In this article, I will describe a simple serverless data pipeline using AWS services.

Architecture

The pipeline can use the following AWS services:
  • Amazon S3 – Stores the raw and processed data.
  • AWS Lambda – Processes the data automatically.
  • AWS Glue – Performs data transformation and cataloging.
  • Amazon Athena – Queries the processed data using SQL.
The basic flow is:
Data Source → Amazon S3 → AWS Lambda → AWS Glue → Amazon Athena

How It Works

1. Store Raw Data in Amazon S3

The raw files are uploaded to an S3 bucket. These files could be CSV or JSON files containing customer, sales, or transaction data.
For example:
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s3://my-data-bucket/raw/sales.csv

2. Process the Data with AWS Lambda

When a new file is uploaded, an S3 event can trigger a Lambda function.
Lambda can perform simple operations such as:
  • Checking the file
  • Removing unwanted records
  • Validating columns
  • Moving processed files to another S3 folder
For example:
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raw/
sales.csv

processed/
sales_cleaned.csv

3. Transform Data with AWS Glue

AWS Glue can be used for larger transformations and ETL jobs.
The data can be transformed into a format that is easier and faster to analyze. Glue Data Catalog can also maintain metadata about the datasets.

4. Query the Data Using Amazon Athena

After processing, Amazon Athena can query the data directly from S3 using SQL.
For example:
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SELECT product, SUM(sales) AS total_sales
FROM sales
GROUP BY product
ORDER BY total_sales DESC;
This allows analysts and data engineers to analyze the processed data without managing database servers.

Why Use a Serverless Pipeline?

A serverless architecture is useful because there is no need to manage servers manually.
Some benefits include:
  • Automatic scaling
  • Pay-as-you-use pricing
  • Less infrastructure management
  • Easy integration between AWS services
  • Suitable for event-driven workloads

Conclusion

A combination of Amazon S3, AWS Lambda, AWS Glue, and Amazon Athena can be used to build a simple and scalable serverless data pipeline.
This architecture is a good starting point for learning AWS data engineering because each service has a specific role in the pipeline.
The main idea is simple:
Store → Process → Transform → Analyze
This small architecture can later be extended with services such as Amazon EventBridge, AWS Step Functions, Amazon Redshift, and Amazon CloudWatch.
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# data# aws-data-pipeline# data-engineer# data-engineering# data-lakes
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