Azure Batch vs. Azure Data Factory

To better serve their consumers, businesses that often interact with those clients who rely on Microsoft's software products may consider migrating to Azure. This program would be useful in any installation of a Microsoft product or suite that necessitates a test of the target environment. It is simple to maintain and implement, making it an ideal IT backbone. If a client doesn't have any use for this particular instrument, it's not going to be of any benefit to them.

Incentivized

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

• Managing the users
• Having multiple environments
• Creating multiple groups

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

• The user interface, in my opinion, might need further clarification.
• Any situation where a user's password has to be reset would benefit from this feature.
• Any accounts that were accidentally established more than once may be transferred over quickly and easily.

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

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

They both are great tools and provide the services they have implemented. They are two competing companies that have different cultures and forward mission agendas. I would say Azure is a little easier to support through their user interface for the IT support side of things. Both tools are useful and have their own strength and weakness. If you're a dynamic company with a multitude of customers then both are a required tool to have.

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

• After initial setup, we now have significantly less time spent processing data.
• The automation of data formatting and display after processing is exciting because it frees us to focus on the data itself.
• Since using Batch, we have significantly decreased the number of items we previously utilized.

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