Azure Data Factory vs. HCL DataConnect

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

The choice to use Actian DataConnect really comes down to pricing and the type of integration project you are performing, specifically with the requirements, whether you want the integration to be on-premise or in the cloud, can you install agents on your servers etc. For integration tools it runs head to head with its competitors.

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

• Map multiple source and destination tables - what would be done in many steps in SSIS can be done in one or two "maps" in Data Integrator.
• Platform independent, cloud based, no client software.
• Conversion of data types, write flat files - this helps us with integrating systems.
• Fairly straightforward to use if the user has ETL experience.

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

• Integration Manager needs better scheduling improvements. Like dependencies, and not running a process again if the prior run is still running.
• New connector development does not seem a priority.

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 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

Data Integrator was initially a part of a package deal for us with our new Student Information System. It was chosen prior to my arrival at the institution. Since I discovered we have SQL Server licenses, we have decided to use SSIS instead since I have a background in the MSBI stack and SSIS appears to be more straightforward (process flow philosophy vs "map" philosophy.)

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

• Our customer likes the consistent reliable integrations we have built so we [got] a definite return on investment.
• Problems are fairly easy to handle and rework.

Incentivized