Azure Data Factory vs. Azure Functions

Best scenario is for ETL process. The flexibility and connectivity is outstanding. For our environment, SAP data connectivity with Azure Data Factory offers very limited features compared to SAP Data Sphere. Due to the limited modelling capacity of the tool, we use Databricks for data modelling and cleaning. Usage of multiple tools could have been avoided if adf has modelling capabilities.

Incentivized

It is well suited for REST APIs that need scalable functionality and for hosting microservices with a short life cycle. It is not well suited for Applications that need real-time features or heavy processing, such as image processing or model training, that need to stay up for longer periods and require compute.

Incentivized

• Data Ingestion - it works very well with numerous data sources.
• Data pipeline orchestration: It is a generic, popular tool for orchestrating data pipelines.
• Works well in Azure ecosystem, Azure services and data platforms like Databricks.
• It is a serverless and scalable solution for cloud data integration.

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

• Granularity of Errors: Sometimes, Azure Data Factory provides error messages that are too generic or vague for us, making it challenging to pinpoint the exact cause of a pipeline failure. Enhanced error messages with more actionable details would greatly assist us as users in debugging their pipelines.
• Pipeline Design UI: In my experience, the visual interface for designing pipelines, especially when dealing with complex workflows or numerous activities, can become cluttered. I think a more intuitive and scalable design interface would improve usability. In my opinion, features like zoom, better alignment tools, or grouping capabilities could make managing intricate designs more manageable.
• Native Support: While Azure Data Factory does support incremental data loads, in my experience, the setup can be somewhat manual and complex. I think native and more straightforward support for Change Data Capture, especially from popular databases, would simplify the process of capturing and processing only the changed data, making regular data updates more efficient

Incentivized

• Timing for creating new instances.

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

Sometimes the Azure dashboard can feel confusing at first, and it can be hard to understand where things are. But overall, it has good integrations, is trustworthy for running there without disruptions, is well-documented and easy to implement, especially for newbies on the team, and has nice automated deployment features.

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 helps us automate to schedule jobs as per customer demands to make ETL triggers when the need arises. Anyone can define the workflow with the Azure Data Factory UI designer tool and easily test the systems. It helped us automate the same workflow with programming languages like Python or automation tools like ansible. Numerous options for connectivity be it a database or storage account helps us move data transfer to the cloud or on-premise systems.

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

• Facilitate better decision-making and improve business processes.
• Optimize business process outcomes by increasing internal efficiency and operational effectiveness.
• Boosts revenue growth while improving business process agility.

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