AWS Glue: Serverless ETL Simplified
AWS Glue: Serverless ETL Simplified
In today’s data-driven world, businesses collect huge amounts of data every second.
But raw data isn’t useful unless it’s organized, cleaned, and made ready for analysis.
This is where ETL comes in — and AWS Glue makes ETL simple, serverless, and fast.
Let’s understand how.
π§ What Is ETL?
ETL stands for:
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Extract – Get data from different sources
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Transform – Clean and format the data
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Load – Store it into a data warehouse or analytics tool
π§ What Is AWS Glue?
AWS Glue is Amazon’s serverless ETL service.
In simple words:
AWS Glue helps you prepare, move, and combine data from different sources — without managing servers.
It’s built to automate much of the work involved in data preparation.
π§° Key Features of AWS Glue
Let’s look at what makes Glue powerful:
✅ 1. Serverless
No need to provision or manage servers. AWS does it for you.
✅ 2. ETL Engine
Write ETL jobs in Python or Scala using Apache Spark under the hood.
✅ 3. Glue Data Catalog
A centralized metadata store to organize and discover datasets.
✅ 4. Job Scheduling
Set ETL jobs to run on a schedule or trigger (like file upload or completion of another job).
✅ 5. Connectors
Glue can connect to:
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S3
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RDS (MySQL, PostgreSQL)
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Redshift
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DynamoDB
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Kafka
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Snowflake
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JDBC sources
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and more
✅ 6. Crawlers
Automatically scan and catalog data from different sources.
π How AWS Glue Works (High-Level View)
Here’s the simplified workflow:
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π§Ή Crawler scans your data (e.g., S3)
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π It stores metadata in the Glue Data Catalog
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✍️ You write or generate ETL code (Spark/Python)
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π Glue runs the ETL job
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πΎ Output goes to your desired target (e.g., S3, Redshift)
π️ AWS Glue Components Explained
Let’s break down the main building blocks.
1. π️ Glue Data Catalog
A centralized place to store metadata (schema, table names, formats, etc.)
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Think of it like a library catalog but for your datasets.
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Helps Glue (and other AWS services like Athena) understand your data.
2. π§ Glue Crawlers
Crawlers are smart tools that:
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Connect to a data source (e.g., S3 bucket)
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Scan files and detect schema automatically
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Create or update metadata tables in the Glue Catalog
They save time by automating data discovery.
3. π§π» Glue Jobs
This is the core ETL code.
You can:
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Use the Glue Studio visual editor
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Write your own Python (PySpark) or Scala code
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Run batch or streaming ETL
Jobs can be triggered manually, by a schedule, or by an event.
4. π§ͺ Glue Studio
A visual interface to create and manage ETL workflows.
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No-code/low-code editor
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Drag-and-drop transforms
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Preview and debug data
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Great for beginners and analysts
5. ⛓️ Workflows and Triggers
You can organize multiple jobs into workflows:
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Run Job A
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Then Job B only if A succeeds
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Send alert if B fails
Triggers can be:
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Time-based (e.g., every hour)
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Event-based (e.g., new file in S3)
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On completion of another job
✨ Benefits of Using AWS Glue
π§π» 1. Developer Friendly
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Python-based scripting (PySpark)
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Built-in transforms for joins, filters, mapping, etc.
π§ 2. Smart Automation
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Automatically generate ETL code
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Detect schema changes
⚙️ 3. Scalability
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Built on Apache Spark
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Automatically scales based on data volume
π 4. Cost-Effective
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Pay-per-second for only the time your jobs run
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No idle server costs
π 5. Integration with AWS Ecosystem
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S3, Redshift, Athena, Lake Formation, Lambda, and more
π Example Use Case
Let’s say you’re a company storing user logs in S3.
You want to:
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Clean and normalize the logs
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Add metadata (like user region)
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Load final data into Redshift for reporting
With Glue:
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Use a crawler to detect the schema in S3
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Write a Glue job (or use Glue Studio) to clean and enrich data
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Output to Redshift
No server setup. Just configure and run.
π§ͺ Sample Glue Job (Python – PySpark)
π Common Use Cases for AWS Glue
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Data lakes: transform and clean S3 data
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Redshift loading: prepare and push data into Redshift
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Data warehousing: unify data from various sources
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Real-time analytics: with Glue streaming jobs
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GDPR/PII masking: clean sensitive data
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Log analytics: process structured and unstructured logs
⚠️ Limitations to Know
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Glue job start time can take 30–60 seconds (cold start)
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Advanced Spark tuning may be limited
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Streaming jobs in Glue are newer (still evolving)
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Requires some knowledge of Spark for custom jobs
π° AWS Glue Pricing (Simplified)
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Crawler: $0.44 per DPU-hour
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ETL Job: $0.44 per DPU-hour
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Data Catalog: Free for up to 1 million objects/month
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Streaming Jobs: $0.44 per DPU-hour (charged per second)
π‘ DPU = Data Processing Unit (1 DPU = 4 vCPU + 16 GB RAM)
πͺ Tips for Using AWS Glue Effectively
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Use Glue Studio for faster development
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Store raw + processed data in S3 as Parquet (efficient format)
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Use partitioning to improve performance
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Keep schema consistent to avoid crawler issues
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Monitor jobs with CloudWatch
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Use IAM roles carefully for secure access
π AWS Glue vs Other ETL Tools
Feature | AWS Glue | Apache Airflow | Talend | Informatica |
---|---|---|---|---|
Serverless | ✅ Yes | ❌ No | ❌ No | ❌ No |
Cost-effective | ✅ Pay-per-use | ❌ Costly infra | ❌ License | ❌ Expensive |
Auto Cataloging | ✅ Yes | ❌ No | ❌ Partial | ❌ No |
Deep AWS Integration | ✅ Full | ✅ Medium | ✅ Some | ✅ Some |
Coding Needed | ❌ Low (Studio) | ✅ Yes | ❌ Low-code | ❌ No-code |
π Learning Resources
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YouTube Channels like AWS Events, Stephane Maarek, and freeCodeCamp
π§Ύ Final Thoughts
AWS Glue simplifies data movement and transformation.
Whether you’re building data lakes, pipelines, or reports — it saves you time, effort, and cost.
With serverless power, deep AWS integration, and visual tools, it’s perfect for:
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Data engineers
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Analysts
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Developers
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Enterprises of all sizes
Clean, transform, and deliver your data — all with AWS Glue.
No servers. No headaches. Just pure ETL.
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