Nowadays, it’s hard to imagine effective Business Intelligence without ETL (extract, transfer, and load) tools. These tools help you integrate data from multiple sources into a single database environment. Instead of keeping data in silos, you get an efficient and consistent data warehousing method where all data brings you value, regardless of its format and source. By integrating data from multiple sources into a single database environment, you can automate data management and save valuable time, allowing you to focus on critical business tasks.
When selecting the ETL tool that fits you most, there’s typically a dilemma between AWS Glue and Azure Data Factory. These are the major ETL solutions provided by the world’s biggest cloud providers, AWS and Microsoft Azure, respectively. In this article, we’ll provide an overview of both solutions, compare them, and help you determine which ETL tool to choose and when.
Introduction to AWS Glue and Azure Data Factory
AWS Glue is Amazon’s primary ETL tool that enables users to collect, process, and move data across data pipelines seamlessly. As a serverless offering, AWS Glue is a managed solution where most configurations and maintenance are taken care of by the provider. Users only need to define a data pipeline and the processes they want to run with AWS Glue.
Azure Data Factory (ADF) is Microsoft Azure’s primary ETL service that streamlines data processing and transfers data through custom pipelines, similar to AWS Glue. Azure Data Pipeline is a managed serverless solution where Azure handles most infrastructure management and configuration tasks. This enables users to focus on building and developing data pipelines instead of maintaining them.
At ABCloudz, we pride ourselves as experts in ETL script configuration, and we can help you choose the best ETL tool that fits your unique business requirements.
ABCloudz as a perfect data integration team
At ABCloudz, we have exceptional expertise in configuring ETL tools, their jobs, and scripts. Our skills and expertise are built on over 10 years of experience in configuring, managing, and building data solutions for businesses operating in various industries, ranging from healthcare to e-commerce. As an AWS Select Tier Consulting Partner and a team with 30+ Microsoft-certified employees, we possess a rich arsenal of practices for configuring your ETLs and providing you with excellence in data integration and business intelligence configuration.
Comparing AWS Glue and Azure Data Factory
Now, let’s compare the two ETL tools by their most critical parameters.
| AWS Glue | Azure Data Factory |
|---|---|
| Performance | |
| AWS Glue provides the smooth operation of Apache Spark jobs. However, if you want to improve your performance, there are multiple techniques at your disposal, including partitioning for querying. You can also use auto-scaling features for this purpose. | Azure Data Factory delivers stable and efficient performance. If the load grows dramatically, you can add another integration runtime and specify a higher capacity to adapt to the growing workloads and keep stable performance. |
| Manageability | |
| AWS Glue is a managed serverless solution where users are only required to specify their data pipeline. | Azure Data Factory is also a managed serverless solution. However, it still requires the users to add Azure Data Factory to a repository, configure integration runtime, configure the connected services, create the pipeline, and configure the scheduler for the pipelines. |
| Pricing | |
| AWS Glue is currently priced at $0.44 per DPU-Hour or $0.29 per DPU-Hour for flexible jobs. Additional charges may apply for data extraction from data sources and data catalog storage, while no supplementary fees are incurred for services such as pipeline runs. | Several factors affect Azure Data Factory cost. Azure levies a charge of $0.25 for each data integration unit, which is comparable to DPUs. Separate fees are imposed based on the duration of pipeline operation, data read/writes, and total pipeline runs. |
| Features | |
Some critical and distinctive AWS Glue features include:
| Some critical and distinctive Azure Data Factory features include:
|
| High availability | |
| AWS Glue Availability Zones allow users to create and manage applications and databases that can switch between Availability Zones automatically, ensuring uninterrupted service in the event of a failure. | In case of a disaster, Azure Data Factory pipelines can be transferred automatically to the paired region without any customer intervention, provided that the ADF team confirms the outage. |
| Connectors | |
| AWS Glue runs with all connectors from the AWS Marketplace and variosu custom connectors. Although Glue has the capability to integrate with several widely used Microsoft-based data stores, such as SharePoint, it currently lacks connectors for certain stores like Microsoft Access. | Azure Data Factory supports connectors for all Microsoft products, as well as connectors from many other services, including AWS. |
| SSIS support | |
| To use SSIS packages with AWS Glue, you shoud convert them with Glue scripts or Glue Studio. | Azure Data Factory is perfectly compatible with SSIS. It supports SSIS packages and allows deploying them directly. |
| Environment | |
| AWS Glue runs its jobs only in the Apache Spark runtime environment. To run jobs outside the Apache Spark environment, you shoud use the Glue Python Shell job. | Azure Data Factory runs its jobs in all services of the Apache Hadoop Cluster. |
Use cases for AWS Glue
From our expertise, here’s the list of the main cases when we recommend you use AWS Glue.
Big Data analytics
If you require fast, fully-managed, and customizable integration for Big Data analytics, AWS Glue is the perfect solution for you. It provides access to excellent analytical tools like Amazon Kinesis, as well as services such as Amazon S3 and Amazon Redshift, which are beneficial for Big Data practices. Additionally, AWS DataBrew, which seamlessly integrates with AWS Glue, makes it a more convenient solution for visualization in analytics.
Application development in the AWS environment
If you rely on AWS services to build your data infrastructure and require data integration for developing applications in the AWS environment, AWS Glue is the ideal fit. It provides higher levels of customizability than Azure Data Factory, which can be advantageous for app development.
Machine learning
AWS Glue if you need data integration for machine learning. Amazon provides a rich arsenal of free services that will help you build machine learning features more efficiently.
Data warehousing
With Amazon Redshift being one of the most advanced data warehousing technologies in the market, AWS Glue, which ensures seamless data integration with Amazon Redshift, is the perfect ETL tool for data warehousing.
Use Cases for Azure Data Factory
Meanwhile, we recommend you go with Azure Data Factory in the cases outlined below.
Data integration in the Microsoft environment
If you rely on Microsoft services for developing your applications or data systems, Azure Data Factory is the best ETL solution for you. It is completely code-free, simplifying data integration.
Reliance on the entire Apache Hadoop Cluster
Azure Data Factory allows you to run data integration jobs in any of the services in the Apache Hadoop cluster, unlike AWS Glue, which is restricted to Apache Spark.
Data migration
Both AWS Glue and Azure Data Factory are capable of running with SSIS, which is essential for data migration. However, Azure Data Factory has better integration with SSIS than AWS Glue. Note that AWS Glue would still be a better data migration ETL tool if you rely on the AWS environment.
Real-time data integration monitoring
Azure Data Factory provides an excellent range of monitoring practices that are especially important for real-time data integrations. In this regard, Azure Data Factory slightly prevails over AWS Glue.
Deciding between AWS Glue and Azure Data Factory
In conclusion, both tools have their evident benefits. AWS Glue may be a better fit for data integrations that require more customizability, complex application development, and big data processing. In addition, AWS Glue is more predictable in terms of pricing. Prices for most ETL jobs are fixed, while in Azure Data Factory, they depend on a great range of additional features and services. Finally, AWS Glue fits highly-diverse data infrastructure involving different databases with which AWS perfectly integrates.
On the flip side, Azure Data Factory will be your perfect match if you rely on Microsoft products and services, need advanced data monitoring capabilities, and want to skip a DevOps stage, avoid coding, and minimize configuration. Also, if your architecture mostly relies on Microsoft products, ADF will be your perfect match.
Ultimately, both are solid solutions that serve the same purposes. The most critical factor behind your decision should be a more general choice between AWS and Microsoft Azure. Regardless of the decision you make, ABCloudz is capable, experienced, and ready to help you configure cloud environment and organize state-of-the-art data integration with either AWS Glue or Azure Data Factory. Contact us now and see how we can help you achieve data integration goals.
