AWS Data Pipeline vs AWS Glue: Choosing the Best ETL Tool for AWS


AWS Data Pipeline vs AWS Glue | Hevo Data

Are you trying to choose between AWS Data Pipeline and AWS Glue? Are you figuring out how these AWS ETL tools differ in features, application, pricing, etc.? Do you want to know which service best suits your organizational needs? If so, then you are at the right place. 

This blog will compare two popular ETL solutions from AWS: AWS Data Pipeline vs. AWS Glue. It will examine their nuanced differences and help you zero in on one.

Table of Contents

What is AWS Data Pipeline?

AWS Data Pipeline is a web service on the Amazon Cloud that helps you automate your data movement processes. This is done through workflows that make subsequent data tasks dependent on the successful completion of preceding tasks. These workflows make it possible for you to automate and enhance your organization’s ETL on the AWS cloud. Thus, you are able to take advantage of already-existing configurations on the Cloud. AWS Data Pipeline also offers a drag-and-drop User Interface (UI) and gives users full control of the computational resources behind their Data Pipeline logic. You could also read ‘What is Data Pipeline?‘ for more details on data pipelines.

Simply put, AWS Data Pipeline is an AWS service that helps you transfer data on the AWS Cloud by defining, scheduling, and automating each task.

For example, you can build a data pipeline to extract event data from a data source on a daily basis and then run an Amazon EMR (Elastic MapReduce) over the data to generate EMR reports. 

A tool like AWS Data Pipeline helps you transfer and transform data spread across numerous AWS tools and also enables you to monitor it from a single location.

Key Features of AWS Data Pipeline

  • It is easy to debug or change your data workflow logic since AWS Data Pipeline allows you to exert full control over the compute resources that execute your business logic. 
  • It has an architecture with high availability and fault tolerance. Thus, it can run and monitor your processing activities effectively.
  • AWS Data Pipeline is highly flexible. You can write your own conditions/activities or use the in-built ones to take advantage of the platform’s features, such as scheduling, error handling, etc.
  • It provides support for a wide variety of data sources ranging from AWS to on-premises data sources.
  • In addition to transferring your data, AWS Data Pipeline enables you to define activities like HiveActivity (will run a Hive query on an EMR cluster), PigActivity (runs a Pig script on an EMR cluster), SQLActivity (runs a SQL query on a database), EMRActivity (runs an EMR cluster), etc. to help you process or transform your data on the cloud.

To know more about AWS Data Pipeline, visit this link.

ETL Your Data Seamlessly Using Hevo’s No-code Data Pipeline

Hevo Data, an Automated No-code Data Pipeline, helps you directly transfer data from Databases, CRMs, SaaS Platforms, and 150+ other sources (50+ free sources) to Data Warehouses, Databases, or any other destination of your choice in a completely hassle-free manner. Hevo offers end-to-end Data Management and completely automates the process of collecting your decentralized data and transforming it into an analysis-ready form. Its fault-tolerant architecture ensures high Data Quality and Data Governance for your work without having to write a single line of code.

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Hevo is fully managed and completely automates the process of not just ingesting data from multiple sources but also enriching the data and transforming it into an analysis-ready form without any manual intervention. It is a consistent & reliable cloud-based solution to manage data in real-time and always have analysis-ready data in your desired destination. Hevo takes care of your complex ETL processes and allows you to focus on key business needs and data analysis using a BI tool of your choice (add native integrations)

What is AWS Glue?

AWS Data Pipeline vs AWS Glue: AWS Glue Logo | Hevo Data
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AWS Glue is a serverless, fully managed ETL service on the Amazon Web Services platform. It provides a quick and effective means of performing ETL activities like Data Cleansing, Data Enriching, and Data Transfer between data streams and stores. AWS Glue was built to work with semi-structured data and has three main components: Data Catalog, ETL Engine, and Scheduler. It also has a feature known as “Dynamic Frame.” A Dynamic Frame is a data abstraction that organizes your data into rows and columns where each record is self-describing and does not require users to specify a schema initially. 

At a higher level, AWS Glue Data Catalog is a Big Data cataloging tool that enables you to perform ETL on the AWS Cloud.

For example, you can use the Glue User Interface to create and run an ETL job in the AWS Management Console and then point AWS Glue to your data. AWS Glue will then store your metadata in the Data Catalog and generate a code to perform data transformations and loading.

Key Features of AWS Glue

  • AWS Glue can automatically generate code to perform your ETL after you have specified the location or path where the data is being stored.
  • With AWS Glue, you can set up crawlers to connect to data sources. This helps classify the data, obtain the schema, and automatically store it in the data catalog for ETL jobs.
  • It is easy to set up continuous ingestion pipelines for preparing streaming data on the fly with the help of Glue’s serverless streaming ETL function. The streaming data is also available for analysis within seconds. This feature makes it easy to process event data such as clickstreams, network logs, etc.
  • Furthermore, AWS Glue also has an integrated Data Catalog with table definitions and other control information that helps you manage your AWS Glue environment. The data catalog will automatically compute statistics and register partitions to make your queries efficient and cost-effective. 
  • Glue can also clean and prepare your data through FindMatches, its transform feature. FindMatches helps you locate matching records and dedupe your data. 

To know more about AWS Glue, visit this link.

Factors that Drive AWS Data Pipeline vs. AWS Glue Decision

Now that you are familiar with AWS Data Pipeline and AWS Glue, let’s directly compare the 2 services. These are the top parameters to consider when contemplating AWS Data Pipeline vs. AWS Glue:

1) AWS Data Pipeline vs. AWS Glue: Infrastructure Management

AWS Glue is serverless, so developers have no infrastructure to manage. Glue’s Apache Spark environment fully manages scaling, provisioning, and configuration. 

AWS Data Pipeline is not serverless like Glue. It launches and manages the lifecycle of EMR clusters and EC2 instances to execute your jobs. You can define the pipelines and have more control over the compute resources underlining them.

These are important factors while doing an AWS Data Pipeline vs. AWS Glue comparison, as this will determine the kind of skills and bandwidth you would need to invest in your ETL activities on the AWS cloud.

2) AWS Data Pipeline vs. AWS Glue: Operational Methods

AWS Glue provides support for Amazon S3, Amazon RDS, Redshift, SQL, and DynamoDB and also provides built-in transformations. On the other hand, AWS Data Pipeline allows you to create data transformations through APIs and also through JSON, while only providing support for DynamoDB, SQL, and Redshift. 

Additionally, AWS Glue supports the Apache Spark framework (Scala and Python), while AWS Data Pipeline supports all the platforms supported by EMR and Shell. 

Data transformation functionality is a critical factor while evaluating AWS Data Pipeline vs. AWS Glue, as this will impact your particular use case significantly.

3) AWS Data Pipeline vs. AWS Glue: Compatibility / Compute Engine

AWS Glue runs your ETL jobs on its virtual resources in a serverless Apache Spark environment. 

AWS Data Pipeline does not restrict to Apache Spark and allows you to use other engines like Pig, Hive, etc. This makes it a good choice for your organization if your ETL jobs do not require the use of Apache Spark or multiple engines. 

What Makes Your Data Integration Experience With Hevo Unique?

Hevo helps customers not only move all their data into their preferred Data Warehouse without having to write any code. It offers top-class Data Ingestion and Data Replication services. Compared to AWS Data Pipeline and AWS Glue‘s support for limited sources, Hevo allows you to set up data integration from 150+ Data Sources (including 50+ Free Data Sources). On top of that, Hevo offers you a flexible and transparent pricing plan where you don’t have to pay for storage and infrastructure.

These are some other benefits of having Hevo Data as your Data Automation Partner:

  • Exceptional Security: A Fault-tolerant Architecture that ensures Zero Data Loss.
  • Built to Scale: Exceptional Horizontal Scalability with Minimal Latency for Modern-data Needs.
  • Built-in Connectors: Support for 100+ Data Sources, including Databases, SaaS Platforms, Files & More. Native Webhooks & REST API Connector available for Custom Sources.
  • Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
  • Smooth Schema Mapping: Fully-managed Automated Schema Management for incoming data with the desired destination.
  • Blazing-fast Setup: Straightforward interface for new customers to work on, with minimal setup time.

Use Hevo’s no-code data pipeline to seamlessly ETL your data from multiple sources to the destination of your choice in an automated way. Try our 14-day full feature access free trial!

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4) AWS Data Pipeline vs. AWS Glue: Pricing

Pricing is one of the most important factors to consider when deciding which of the two tools to adopt for your organization. Below is a high-level pricing summary for both services beyond their respective free tiers.

AWS Data Pipeline charges a fee of $1 per month per pipeline if it is run more than once a day and $0.68 per month per pipeline if run one time or less per day. You are also required to pay for EC2 and any other resources you may consume. 

AWS Glue charges $0.44 per Data Processing Unit hour, billed at every second of use. Data Processing Units are consumed when you run crawlers or jobs. $1 is also charged per 100,000 objects that you manage in the data catalog, and also $1 per million requests to the data catalog.

When considering comparing AWS Data Pipeline with AWS Glue, it is important to consider the type, frequency, and objects involved in your ETL activity, as this will significantly impact your costs.

5) AWS Data Pipeline vs. AWS Glue: Use Cases

AWS Data Pipeline transforms and moves data across AWS components. It also gives you control over the compute resources that run your code and allows you to access the Amazon EMR clusters or EC2 instances. For example, you can use AWS Data Pipeline to create a template to move DynamoDB tables from one region to another with EMR.

AWS Glue is best used to transform data from its supported sources (JDBC platforms, Redshift, S3, RDS) to be stored in its supported target destinations (JDBC platforms, S3, Redshift). Using Glue also lets you concentrate on the ETL job as you do not have to manage or configure your compute resources. For example, you can infer a schema from data in an S3 location and build a virtual table through Glue Crawler. You can then run a Glue transformation job and also use the JDBC drivers to connect to Athena to query the virtual table.

AWS Data Pipeline vs. AWS Glue vs. Hevo

You can get a better understanding of Hevo’s Data Pipeline as compared to AWS Glue & AWS Data Pipeline using the following table:

S.noParametersAWS Data
AWS GlueHevo Data
1)SpecializationData TransferETL, Data CatalogETL, Data Replication,
Data Ingestion
2)PricingPricing depends on your frequency of usage and whether you use AWS or an on-premise setup.AWS Data Catalog charges monthly for storage while AWS Glue ETL charges hourly.Hevo follows a flexible & transparent pricing model where you pay
as you grow. Hevo offers 3 tiers of pricing, Free, Starter & Business. Check out the details here.
3)Data ReplicationFull table; Incremental replication via Timestamp
Full table; Incremental via Change Data Capture (CDC) through AWS Database Migration Service (DMS).Full table; Incremental via SELECT or Replication key, Timestamp & Change Data Capture (CDC).
AWS Data Pipeline supports only 4 sources namely, DynamoDB, SQL, Redshift, and S3.AWS Glue caters to Amazon platforms such as Redshift, S3, RDS, DynamoDB, AWS destinations, and other databases via JDBCHevo has native connectors with 150+ data sources and integrates with Redshift, BigQuery, Snowflake, Databricks, and other Data Warehouses & BI tools. Check out the complete integrations list here.


This article introduced you to AWS Data Pipeline and AWS Glue and explained their key features. It also discussed the 5 key parameters that will help you conclude the AWS Data Pipeline vs. AWS GLue discussion. While both of these services can be used to assist with your organization’s ETL tasks, they are best suited for their specific use cases and applications, as outlined above. So it is imperative that you carefully consider each of these points before deciding the one that is right for your needs and organizational requirements.

Hevo is an all-in-one cloud-based ETL pipeline that will help you transfer data and transform it into an analysis-ready form. Hevo’s native integration with 150+ sources (including 50+ free sources) ensures you can move your data without writing complex ETL scripts. Hevo’s automated data transfer, data source connectors, and pre-post transformations are advanced compared to Apache airflow. It will make your life easier and make data migration hassle-free.

Learn more about Hevo

What is your preferred ETL tool for your AWS cloud platforms? Let us know in the comments section.

Rashid Y
Freelance Technical Content Writer, Hevo Data

Rashid is passionate about freelance writing within the data industry, and delivers informative and engaging content on data science by incorporating his problem-solving skills. He finds joy in breaking down complex topics for helping data practitioners solve their everyday problems.