StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. AWS Glue vs Azure Data Factory

AWS Glue vs Azure Data Factory

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
AWS Glue
AWS Glue
Stacks461
Followers819
Votes9

AWS Glue vs Azure Data Factory: What are the differences?

AWS Glue and Azure Data Factory are both cloud-based data integration services offered by AWS and Microsoft respectively. These services enable organizations to orchestrate and automate the process of data extraction, transformation, and loading (ETL) in a scalable and efficient manner.

  1. Pricing Model: AWS Glue follows a pay-as-you-go pricing model, where users are billed based on the amount of data processed and the number of compute resources used. Azure Data Factory also adopts a similar pricing structure, charging based on data integration activity and the number of pipeline runs.

  2. Connectivity: AWS Glue supports a wide range of data sources, including popular AWS services such as Amazon S3, Amazon RDS, and Amazon Redshift, as well as various on-premises data stores. Azure Data Factory, on the other hand, integrates well with Microsoft services like Azure Blob Storage, Azure SQL Database, and Azure Data Lake Store, along with other external data sources.

  3. Data Transformation Capabilities: Both AWS Glue and Azure Data Factory provide robust data transformation capabilities. AWS Glue supports the use of Python and Spark-based ETL scripts for complex transformations, and it offers a visual interface for creating and managing workflows. Azure Data Factory offers a drag-and-drop interface for building data transformation pipelines and supports built-in data integration and transformation activities.

  4. Data Movement and Orchestration: AWS Glue leverages scalable serverless technology to automatically generate and execute ETL code, simplifying the process of moving and transforming data. Azure Data Factory also provides a serverless option for data movement and orchestration, enabling users to efficiently transfer data between various sources and destinations.

  5. Monitoring and Governance: Both AWS Glue and Azure Data Factory offer built-in monitoring and logging capabilities. AWS Glue integrates with Amazon CloudWatch to provide detailed monitoring and performance metrics. Azure Data Factory integrates with Azure Monitor and Azure Log Analytics for monitoring and logging purposes.

  6. Ecosystem and Integration: AWS Glue seamlessly integrates with other AWS services, allowing users to leverage additional capabilities such as data cataloging, data quality checks, and metadata management. Azure Data Factory integrates well with the Microsoft Azure ecosystem, enabling users to leverage services like Azure Machine Learning, Azure Databricks, and Azure Functions for advanced data processing and analytics.

In summary, both AWS Glue and Azure Data Factory provide powerful data integration and ETL capabilities in a cloud environment, with differences primarily lying in their pricing models, connectivity options, data transformation capabilities, data movement and orchestration methods, monitoring and governance features, and ecosystem integrations.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Azure Data Factory, AWS Glue

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

269k views269k
Comments
datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k
Comments
Pavithra
Pavithra

Mar 12, 2020

Needs adviceonAmazon S3Amazon S3Amazon AthenaAmazon AthenaAmazon RedshiftAmazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

522k views522k
Comments

Detailed Comparison

Azure Data Factory
Azure Data Factory
AWS Glue
AWS Glue

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere.
Statistics
GitHub Stars
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
461
Followers
484
Followers
819
Votes
0
Votes
9
Pros & Cons
No community feedback yet
Pros
  • 9
    Managed Hive Metastore
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
Amazon Redshift
Amazon Redshift
Amazon S3
Amazon S3
Amazon RDS
Amazon RDS
Amazon Athena
Amazon Athena
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
Amazon EMR
Amazon EMR
Amazon Aurora
Amazon Aurora
Oracle
Oracle
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL

What are some alternatives to Azure Data Factory, AWS Glue?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase