Azure Data Factory vs AWS Glue-The Cloud ETL Battle

Azure Data Factory vs AWS Glue-Understand the pros and cons, use cases, and integration options of these powerful ETL tools | ProjectPro

Azure Data Factory vs AWS Glue-The Cloud ETL Battle
 |  BY Daivi

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. Azure Data Factory and AWS Glue are powerful tools for data engineers who want to perform ETL on Big Data in the Cloud.


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Azure Data Factory and AWS Glue are two competing serverless options from the two largest cloud service providers, and both employ Spark as an underlying tech stack. They are PaaS-based ETL and ELT solutions. With a lot of similarities between both, but there are some interesting differences too, and thus, professionals often consider comparing them so they can choose the right ETL tool for their projects. What follows is a detailed comparison of Azure Data Factory vs. AWS Glue based on several aspects to help you choose the right platform for your big data project needs. In addition, it also covers a section focusing on their similarities. So, let’s dive in! 

What is Azure Data Factory? 

Azure Data Factory is a cloud-based data integration tool that lets you build data-driven processes in the cloud to orchestrate and automate data transfer and transformation. ADF itself does not save any data. It enables you to design data-driven workflows to manage data transfer between supported data stores and subsequently analyze the data using computing services in other regions or in an on-premise environment. It also enables you to monitor and manage workflows using programmatic and graphical methods.

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What is AWS Glue? 

AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies the preparation and loading of data for analytics. AWS Glue provides the functionality required by enterprises to build ETL pipelines. The user only needs to define a data pipeline and the processes they want to perform when data flows through it.

Azure Data Factory vs. AWS Glue: Key Differences

Let us explore the key differences between the services based on specific features such as pricing, SSIS, etc. 

 

Feature

Azure Data Factory

AWS Glue

Cloud Provider

Microsoft Azure

Amazon Web Services

Data Integration

ETL and ELT support

ETL only

Data Movement

Yes

Yes

Data Orchestration

Yes

Yes

Data Catalog

Azure Data Catalog

Glue Data Catalog

Pricing

Pay-as-you-go

Pay-as-you-go

Scalability

High

High

Integration

Integration with other Azure services like Azure Data Lake Storage, Azure SQL Database, etc.

Integration with other AWS services like S3, Redshift, etc.

Programming Language

.NET and Python

Python and Scala

AWS Glue vs. Azure Data Factory Pricing

Glue prices are primarily based on data processing unit (DPU) hours. The DPU price is the same for most AWS Glue tasks and operations. At the time of publication, the DPU charge is $0.44/DPU-Hour in the AWS U.S. East (Ohio) region. Extra charges for extracting data from data sources and storing data catalogs may apply. There are no associated costs for services such as pipeline runs.

There are additional cost factors with Azure Data Factory. Azure costs $0.25 per data integration unit, equivalent to a DPU. Azure charges separately based on the time a pipeline operates, the number of data reads/writes, and the total number of the pipeline runs.

It is important to note that both Glue and Data Factory have a free tier but offer various pricing options to help reduce costs with pay-per-activity and reserved capacity.

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Amazon Glue vs. Azure Data Factory SSIS Support 

ADF provides native support for SSIS packages so its easier to migrate SSIS packages unlike AWS Glue that does not provide native support. Both AWS Glue and Azure Data Factory can import SSIS packages. This is a framework for creating data pipelines that use Microsoft SQL Server. In on-premises systems, SSIS is often used to create data pipelines. Some firms wish to transfer SSIS packages to the cloud as the foundation for serverless ETL workflows.

Importing SSIS packages to AWS Glue takes more time and effort than Azure Data Factory. Glue requires conversion of packages, but Azure Data Factory allows users to install and use SSIS packages directly without converting or migrating them.

Azure Data Factory vs. AWS Glue: Developer Tools 

ADF provides a visual, no-code authoring experience using ADF portal that lets data engineers create, test, and deploy data pipelines while Glue provides the same through AWS Glue console.Python and Scala SDK are developer tools in AWS Glue. ADF features a REST API,.Net and Python SDKs, and a PowerShell CLI as developer tools.

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Amazon Glue vs. Azure Data Factory: Data Replication Support 

Azure Data Factory provides data replication with the use of dataflows while AWS Glue provides data replication through glue jobs. As AWS glue is more focussed on ETL , it provides an easier way to replicate data while ADF provides a more comprehensive and

integrated way to replicate data between different sources and targets with the advanced transformation capabilities of  dataflows.

Azure Data Factory supports database replication at both levels: full table and incremental via a custom SELECT query. On the other hand, Amazon Glue also supports database replication at both levels: full table and incremental via change data capture through AWS Database Migration Service (DMS).

Azure Data Factory vs. AWS Glue: Data Sharing 

ADF allows data sharing with the use of Dataflows while AWS Glue allows data sharing through Glue Data Catalog. The way they differ in terms of data sharing is that Glue provides a centralized repository to share metadata information about data pipelines and and data sources while ADF provides a more comprehensive and integrated way to share data between various services and platforms.

Azure Data Factory vs. AWS Glue: Key Similarities 

The following are the fundamental similarities of both services: 

  • Both are fully-managed serverless offerings that feature ETL engines. 

  • Both services support structured and unstructured data.

  • Both services can generate codes on their own.

  • Both platforms are designed for data transformation and preparation.

  • Both services are capable of cleaning, transforming, and aggregating data.

  • Both services allow you to focus on business logic and data transformation.

  • The core technological stack is Spark in both services.

Explore ProjectPro Repository to work on hands-on data engineering projects that leverage both Azure Data Factory and AWS Glue to help you learn more about the differences between them. 

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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