How to Create Scalable ETL Pipelines Using AWS Glue

 Creating scalable ETL (Extract, Transform, Load) pipelines using AWS Glue enables you to automate and streamline big data processing across services like S3, Redshift, RDS, and Athena. Here's a step-by-step guide to help you understand how to build robust, scalable ETL pipelines with AWS Glue.


🔧 What is AWS Glue?

AWS Glue is a fully managed, serverless data integration service that simplifies discovering, preparing, and transforming data for analytics and machine learning.


Key Components:

Component Description

Crawler Scans data sources and builds the Data Catalog

Data Catalog Central metadata repository

ETL Jobs Scripts to extract, transform, and load data

Triggers Automate job execution

Workflows Orchestrate multiple jobs/crawlers


🧭 Step-by-Step: Building a Scalable ETL Pipeline

✅ 1. Set Up Your Data Sources

You can use:


Amazon S3 (raw data in JSON, CSV, Parquet, etc.)


Amazon RDS (MySQL, PostgreSQL)


Amazon Redshift


Third-party JDBC sources


Example: Raw CSV files in s3://my-bucket/input-data/


✅ 2. Create a Crawler to Discover Metadata

Go to AWS Glue > Crawlers > Add Crawler


Choose S3 path or database


Configure output to store metadata in the Glue Data Catalog


Run the crawler → It will create a table with schema information


📌 Use Glue's partitioning features to handle large datasets efficiently (e.g., by year/month/day).


✅ 3. Create an ETL Job

Choose between:

Visual editor (for simple transforms)


Script editor (for custom PySpark/Scala code)


Example PySpark Script:

python

Copy

Edit

import sys

from awsglue.transforms import *

from awsglue.utils import getResolvedOptions

from pyspark.context import SparkContext

from awsglue.context import GlueContext

from awsglue.job import Job


args = getResolvedOptions(sys.argv, ['JOB_NAME'])


sc = SparkContext()

glueContext = GlueContext(sc)

spark = glueContext.spark_session

job = Job(glueContext)

job.init(args['JOB_NAME'], args)


# Read from S3 (Glue catalog table)

datasource = glueContext.create_dynamic_frame.from_catalog(

    database="my_db",

    table_name="input_table"

)


# Transformation (filter and rename)

transformed = datasource.filter(lambda x: x["status"] == "active")


# Write to target (e.g., another S3 location)

glueContext.write_dynamic_frame.from_options(

    frame=transformed,

    connection_type="s3",

    connection_options={"path": "s3://my-bucket/output-data/"},

    format="parquet"

)


job.commit()

✅ 4. Set Up Triggers or Workflows

Use time-based, event-based (e.g., S3 event), or on-job-success triggers.


Workflows allow chaining multiple jobs and crawlers in a visual DAG.


✅ 5. Scale Your Pipeline

Auto-scaling job workers (set NumberOfWorkers or enable Glue 3.0 Ray)


Use partitioned input data to process in parallel


Store transformed data in Parquet or ORC (columnar formats)


Use Job Bookmarks to only process new/changed data


🧠 Best Practices for Scalable Glue Pipelines

Practice Why It Matters

Use Glue 4.0 or 3.0 Faster execution, support for newer libraries

Partition data Improves read/write performance

Monitor via CloudWatch Debugging and performance tuning

Use DynamicFrames for schema flexibility Ideal for semi-structured data

Use Job Parameters For reusability and environment-specific config

Use Athena or Redshift Spectrum for querying S3 output Serverless analytics


📊 Example Architecture

scss

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Edit

S3 (raw data) ──▶ Glue Crawler ──▶ Data Catalog

                             │

                             ▼

                     Glue ETL Job (PySpark)

                             │

                             ▼

                    S3 (cleaned Parquet data)

                             │

                             ▼

            Query via Athena, Redshift, or ML tools

🛠 Tools & Services You May Use with AWS Glue

Service Purpose

S3 Raw and processed data lake

Athena Query data directly from S3

Redshift Store and query transformed data

CloudWatch Logs and metrics

Step Functions Complex workflows

Lake Formation Security and access control


✅ Summary

Step Action

Set up source Upload raw data to S3 or connect DB

Crawl the source Use Glue Crawler to catalog metadata

Create ETL Job PySpark (or visual), transform data

Save output Store as Parquet in S3

Schedule & scale Use triggers, bookmarks, partitions

Query or load Into Athena, Redshift, or ML pipelines

Learn AWS Data Engineering Training in Hyderabad

Read More

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

Using AWS to Build Scalable and Secure Data Pipelines for Social Media Analytics

Visit Our IHUB Talent Training in Hyderabad

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