Azure Data Factory vs. IBM Cloud Pak for Data

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

Unlike others analytics tool IBM Cloud Pak for Data provides out-of-the-box privacy, model interpretability and fairness monitoring, along with automatic explanation of data and models written in business language. It's a great tool that all business should emulate. Great user experience because of every feature is functional and improved constantly.

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

• The AI services catalog like the Watson Assistant is very good.
• The analytics dashboard featuring all the recent history is very good with IBM.
• Searching for data through the unified search option is super cool.

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

• It is complex; The platform comes with several features from MLOPs to Customer 360 which may take a long time for the user to understand.

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

IBM has healing mechanisms when resource usage is high. This platform performs well, but when it runs out of capacity, it has crashed for many clients. This is innate in its original design

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

• can improve readiness for cloud migration, improve licensing flexibility with IBM, and reduce both hardware purchases and infrastructure management efforts.
• reduces the expenses of internal resources.
• should improve efficiencies, reduce risks, and increase performance

Incentivized