Azure Data Factory vs. Azure Functions

In a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

Incentivized

They're great to embed logic and code in a medium-small, cloud-native application, but they can become quite limiting for complex, enterprise applications.

Incentivized

• Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
• Hybrid data integration is easily and agilely possible through this software.
• It has lot of various useful components

Incentivized

• They natively integrate with many triggers from other Azure services, like Blob Storage or Event Grid, which is super handy when creating cloud-native applications on Azure (data wrangling pipelines, business process automation, data ingestion for IoT, ...)
• They natively support many common languages and frameworks, which makes them easily approachable by teams with a diverse background
• They are cheap solutions for low-usage or "seasonal" applications that exhibits a recurring usage/non-usage pattern (batch processing, montly reports, ...)

Incentivized

• Learning curve for pipeline creation interface.
• Alerting isn't necessarily built in. Had to work around this to meet team needs.
• With GIT enabled, some features can only be done via git, while some need to be done via the portal.

Incentivized

• My biggest complaint is that they promote a development model that tightly couples the infrastructure with the app logic. This can be fine in many scenarios, but it can take some time to build the right abstractions if you want to decouple you application from this deployment model. This is true at least using .NET functions.
• In some points, they "leak" their abstraction and - from what I understood - they're actually based on the App Service/Web App "WebJob SDK" infrastructure. This makes sense, since they also share some legacy behavior from their ancestor.
• For larger projects, their mixing of logic, code and infrastructure can become difficult to manage. In these situations, good App Services or brand new Container Apps could be a better fit.

Incentivized

So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.

Incentivized

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

Incentivized

Azure Data Factory fits well into our overall systems architecture where we already utilize largely Azure services and also Microsoft based products in the on-premises environment. I think cost structure is also very competitive with Azure Data Factory. Most services provide a visual interface for designing ETL workflows, but our team found Azure Data Factory's interface more intuitive.

Incentivized

This is the most straightforward and easy-to-implement server less solution. App Service is great, but it's designed for websites, and it cannot scale automatically as easily as Azure Functions. Container Apps is a robust and scalable choice, but they need much more planning, development and general work to implement. Container Instances are the same as Container Apps, but they are extremely more limited in termos of capacity. Kubernetes Service si the classic pod container on Azure, but it requires highly skilled professional, and there are not many scenario where it should be used, especially in smaller teams.

Incentivized

• Limiting the amount of data moving up and down from the cloud for cloud-native applications.
• Overall simple to use interface which is actually easier for a first time ETL developer than SSIS.

Incentivized

• They allowed me to create solutions with low TCO for the customer, which loves the result and the low price, that helped me create solutions for more clients in less time.
• You can save up to 100% of your compute bill, if you stay under a certain tenant conditions.

Incentivized