Building an ETL Pipeline with AWS Lambda and AWS Glue

🛠 What is an ETL Pipeline?

ETL stands for:


Extract: Get data from a source (e.g., database, S3, API).


Transform: Clean or format the data.


Load: Store the transformed data into a destination (e.g., S3, Redshift, RDS).


🚀 Tools We’ll Use

✅ AWS Lambda:

Serverless compute service.


Good for event-triggered processing.


Can start ETL jobs, process small files, or call APIs.


✅ AWS Glue:

Managed ETL service.


Can handle large-scale data processing using Spark or Python.


Comes with built-in crawlers, job scheduling, and data cataloging.


🔄 Typical ETL Pipeline Workflow

Step 1: Trigger the Process

Use AWS Lambda to monitor an event (e.g., file upload to S3).


When a new file is uploaded, the Lambda function is triggered.


Step 2: Start AWS Glue Job from Lambda

The Lambda function calls an AWS Glue Job (written in PySpark or Python).


Glue then extracts the data (e.g., from S3 or a database).


Step 3: Transform the Data in Glue

Inside the Glue job:


Read the data (CSV, JSON, Parquet, etc.).


Clean or reformat it (e.g., remove nulls, change column names).


Convert formats if needed (e.g., CSV to Parquet for efficiency).


Step 4: Load the Data

Save the transformed data to:


Amazon S3 (as data lake)


Amazon Redshift


Amazon RDS


Any other target


🧱 High-Level Architecture

java

Copy

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S3 (Raw Data)

   ↓ (File Upload Event)

AWS Lambda (Trigger)

   ↓

AWS Glue (ETL Job)

   ↓

S3 / Redshift / RDS (Processed Data)

✅ Sample Lambda Code to Trigger Glue

python

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Edit

import boto3


def lambda_handler(event, context):

    glue = boto3.client('glue')

    

    response = glue.start_job_run(

        JobName='your-glue-job-name',

        Arguments={

            '--input_path': 's3://your-bucket/raw/',

            '--output_path': 's3://your-bucket/processed/'

        }

    )

    

    print("Glue job started:", response['JobRunId'])

✅ Sample Glue Job Snippet (PySpark)

python

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import sys

from awsglue.context import GlueContext

from pyspark.context import SparkContext

from awsglue.job import Job


sc = SparkContext()

glueContext = GlueContext(sc)

spark = glueContext.spark_session


input_path = sys.argv[1]

output_path = sys.argv[2]


# Read data from S3

df = spark.read.csv(input_path, header=True)


# Example transformation

df_cleaned = df.dropna()


# Write the result

df_cleaned.write.parquet(output_path)

📌 Tips

Use Glue Crawlers to automatically catalog data.


Use Glue Triggers or Step Functions for more complex workflows.


Test Lambda with small sample files to avoid timeouts (max 15 minutes).


Use CloudWatch for logging and monitoring. 

Learn AWS Data Engineering Training in Hyderabad

Read More

How to Create Scalable ETL Pipelines Using AWS Glue

ETL vs. ELT: What’s Best for Your AWS Data Pipeline?

AWS Tools for ETL Processes

Using AWS Redshift Spectrum for Cross-Data Warehouse Analytics

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