AWS Glue vs. Azure Data Factory

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

Incentivized

Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.

Incentivized

• It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
• As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
• It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.

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• It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
• We can use linked service in multiple pipeline/data load.
• It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.

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• In-Stream schema registries feature people can not use this more efficiently
• in Connections feature they can add more connectors as well
• The crucial problem with AWS Glue is that it only works with AWS.

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• Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
• Does not yet have parity with SSIS as far as the transforms available.

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Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

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We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue

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AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.

The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.

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• It had a positive impact on the way we build our data lake.
• It is the single source of truth for data structure (schemas/tables/views).

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• It is very useful and make things easier
• Debugging can improve
• Its better suited than other products with the same objective

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