Building an ETL Pipeline with AWS Serverless Components

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In this article, we explore how to build an Extract, Transform, Load (ETL) pipeline using AWS serverless components like AWS Glue, Lambda, EventBridge, and S3. The example demonstrates processing Spotify data via the Spotipy API and integrating it with PySpark and Snowflake for analytics.

🔗 Reference: Spotify ETL pipeline — AWS, PySpark, Snowflake

You Should Know:

1. Key AWS Services for ETL

  • AWS Glue: Serverless data integration service for ETL jobs.
  • AWS Lambda: Event-driven compute for transformations.
  • Amazon EventBridge: Event bus for triggering pipelines.
  • Amazon S3: Scalable storage for raw and processed data.

2. Example Code: AWS Lambda (Python)

import boto3 
import json

def lambda_handler(event, context): 
s3 = boto3.client('s3') 
 Extract data from Spotify API (Spotipy) 
data = extract_spotify_data() 
 Upload to S3 
s3.put_object( 
Bucket='your-bucket-name', 
Key='raw_spotify_data.json', 
Body=json.dumps(data) 
) 
return {'statusCode': 200, 'body': 'Data stored in S3'}

3. AWS Glue PySpark Script (ETL Job)

from awsglue.context import GlueContext 
from pyspark.context import SparkContext

sc = SparkContext() 
glueContext = GlueContext(sc)

Read from S3 
datasource = glueContext.create_dynamic_frame.from_catalog( 
database="spotify_db", 
table_name="raw_data" 
)

Transform 
transformed_data = datasource.apply_mapping([ 
("song_name", "string", "track_name", "string"), 
("artist", "string", "artist_name", "string") 
])

Write to Snowflake 
glueContext.write_dynamic_frame.from_options( 
frame=transformed_data, 
connection_type="snowflake", 
connection_options={ 
"sfUrl": "your-account.snowflakecomputing.com", 
"sfUser": "user", 
"sfPassword": "password", 
"sfDatabase": "spotify_analytics", 
"sfSchema": "public", 
"sfWarehouse": "compute_wh" 
}, 
format="json" 
)

4. Automating with EventBridge

aws events put-rule --name "TriggerETL" --schedule-expression "rate(1 day)" 
aws events put-targets --rule TriggerETL --targets "Id"="1","Arn"="arn:aws:lambda:us-east-1:123456789012:function:SpotifyETL"

What Undercode Say

Serverless ETL pipelines on AWS provide scalability, cost-efficiency, and automation. By leveraging Glue, Lambda, and EventBridge, organizations can process streaming data without managing infrastructure. Future enhancements could include real-time analytics with Kinesis or ML-powered insights with SageMaker.

Expected Output:

✅ Extracted Spotify data stored in S3

✅ Transformed data loaded into Snowflake

✅ Automated daily ETL execution via EventBridge

Prediction

As serverless architectures evolve, we’ll see tighter integration between streaming ETL and AI/ML workflows, enabling real-time decision-making from live data sources.

Would you like a deeper dive into any specific AWS service used here? 🚀

References:

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