Comparing AWS Glue and AWS Data Pipeline for ETL: Choosing the Right Tool for Your Data Workflows

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In the world of cloud-based data integration and ETL (Extract, Transform, Load), AWS Glue and AWS Data Pipeline stand out as two core services provided by Amazon Web Services. Both tools enable users to move and transform data across AWS resources, but they differ in their design, functionality, and use cases. Whether you're a data engineer building pipelines or a business analyst working on analytics projects, choosing the right tool is critical for efficiency, scalability, and cost optimization. 

In this article, we’ll compare AWS Glue and AWS Data Pipeline across several dimensions to help you make the right decision for your data workflows. This is especially valuable for learners and professionals looking to deepen their ETL expertise through platforms like AccentFuture’s cloud and data engineering training programs. 

What Is AWS Glue? 

AWS Glue is a serverless data integration service that simplifies the process of discovering, preparing, and combining data. It’s designed for modern data architectures, particularly in big data and analytics. 

Key Features of AWS Glue: 

  • Serverless (no infrastructure to manage) 

  • Automatic schema discovery (Data Catalog) 

  • Built-in support for Apache Spark 

  • Integrated development environment with Glue Studio 

  • Jobs can be written in PySpark or Scala 

  • Handles semi-structured data like JSON or Parquet 

  • Built-in support for job scheduling and workflow orchestration 

AWS Glue is ideal for big data processing and complex ETL tasks using Spark, making it popular in use cases involving data lakes, machine learning, and batch data pipelines. 

What Is AWS Data Pipeline? 

AWS Data Pipeline is a workflow orchestration service that helps schedule and automate the movement and transformation of data. It allows users to define data-driven workflows that can be executed on services like Amazon EC2 or Amazon EMR. 

Key Features of AWS Data Pipeline: 

  • Supports data movement between on-premise and AWS 

  • Customizable and script-driven 

  • Allows scheduling and retry logic 

  • Can use EC2 instances for custom workloads 

  • Supports SQL-based transformations with RDS 

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AWS Data Pipeline is often used for more traditional ETL workloads and is suitable when working with legacy systems or more custom data processing logic. 

Key Differences: AWS Glue vs AWS Data Pipeline 

Let’s break down the key differences based on several important aspects: 

Feature 

AWS Glue 

AWS Data Pipeline 

Processing Engine 

Apache Spark 

EC2/EMR or Shell scripts 

Serverless 

Yes 

No 

Ease of Use 

High (Glue Studio) 

Medium (JSON-based templates) 

Supported Languages 

Python (PySpark), Scala 

Shell, SQL, Java 

Monitoring 

CloudWatch, Glue Console 

CloudWatch, Logs 

Data Catalog 

Integrated AWS Glue Catalog 

Must define metadata manually 

Use Case 

Big Data ETL, data lakes 

Custom scripts, legacy data movement 

Learning Curve 

Lower for Spark users 

Higher for custom workflows 

 

When to Use AWS Glue 

You should consider AWS Glue if: 

  • You need to process large-scale datasets using Spark. 

  • You’re building a modern data lake architecture on AWS. 

  • You want a low-maintenance, serverless ETL solution. 

  • You work with structured and semi-structured data (CSV, JSON, Parquet). 

  • You prefer to work in PySpark and benefit from pre-built transformations. 

Example: Suppose you have a massive set of clickstream logs stored in Amazon S3. You want to clean, filter, and prepare this data for analytics in Amazon Redshift. AWS Glue is perfect for this task, thanks to its seamless integration with S3 and serverless execution of Spark jobs. 

When to Use AWS Data Pipeline 

Choose AWS Data Pipeline if: 

  • You require fine-grained control over job execution (e.g., shell scripts or custom EC2 instances). 

  • You're dealing with on-premises data or older systems. 

  • You need to move small to medium-sized datasets on a regular schedule. 

  • You have workflows that involve complex dependencies between steps. 

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Example: You need to move daily transaction logs from an on-premise MySQL database to Amazon S3, then run a custom script to anonymize data. AWS Data Pipeline allows setting up this sequence with retry logic and time-based scheduling. 

Conclusion: Which One Should You Choose? 

Both AWS Glue and AWS Data Pipeline serve the core purpose of moving and transforming data across AWS services. However, the choice depends on your project needs: 

  • For modern, scalable, serverless ETL, AWS Glue is the clear winner. 

  • For custom workflows and legacy integration, AWS Data Pipeline offers more flexibility. 

At AccentFuture, our ETL and data engineering courses cover both tools, giving learners hands-on experience with real-world scenarios. Whether you’re a beginner or an advanced practitioner, mastering AWS Glue and Data Pipeline will give you a competitive edge in the cloud data ecosystem. 

Ready to Upskill? 

Explore our aws data engineer training , tailored to help you become proficient in cloud-based ETL and big data analytics. Start your learning journey with AccentFuture today and build workflows that power the future of data! 

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