Azure Data Factory vs. Incorta

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 best aspect of Incorta is the ability to retain fidelity to raw data sources. Incorta does not require an ETL process or pre-aggregation to get results. Another great feature is the Business Schema. The ability to present data in localized business language is key to citizen development. The solution is a complete platform from ingestion to presentation. There is no need for additional products to provide high-performing dashboards. It is worth repeating that no ETL is required. If you need high-speed ingestion and presentation of millions of rows of data while retaining access to the raw data, Incorta is the right platform.

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

• Speed - ingestion and delivery of data
• Customization
• Support
• Training

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

• Broader use of formula columns in insights
• Formula enhancements to all commenting
• Greater formatting of Insights

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

There are improvements to the formula area that would make this rating go higher. Formatting is also in need of enhancement.

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

I have interacted with support on many occasions. They are professional and knowledgeable and have solved every issue and asked every question or directed me to the appropriate resource who can.

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

Each of the other solutions required an ETL to prepare data for ingestion and presentation. Our requirements were for no ELT. Attempting to use raw data sources with both of the other solutions did not provide the performance required (or in the case of Tableau, never finished.) Incorta provides the capability to utilize citizen developers. The other solutions required technical skills beyond the average citizen developer.

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

• Visibility to daily operations and pacing
• Visibility on daily ACH return facilitating action per client
• Client reporting accepted very well

Incentivized