Azure Data Factory vs. CA Workload Automation

Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.

Incentivized

CA Workload Automation is well suited for firms having to manage and monitor multiple applications operating in multiple environments. and having SLA requirements. CA Workload Automation is less appropriate and not cost-effective for non-critical applications and/or jobs. CA Workload Automation is ideal for companies that require reliable and stable Production support for their business critical jobs to finish on time.

Incentivized

• It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
• We can use linked service in multiple pipeline/data load.
• It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.

Incentivized

• All batch jobs can be monitored from one dashboard.
• Provides detailed error logs.
• We have developed packages to invoke OIC integrations through Autosys.
• Notifications is a good feature when job fails or runs beyond cutoff time.

Incentivized

• Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
• Does not yet have parity with SSIS as far as the transforms available.

Incentivized

• Monitoring of front-end websites.
• More utilization insights if possible.
• Customer service via chat in the future if possible.

Incentivized

Very happy!

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

It would be great if customer chat experience is also readily available.

Incentivized

The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.

Incentivized

We selected CA Workload Automation for the ease of integration and less time to setup. It has less overhead to manage the application and is a very robust application.

Incentivized

• It is very useful and make things easier
• Debugging can improve
• Its better suited than other products with the same objective

Incentivized

• CA Workload Automation has improved SLA by 50%.
• CA Workload Automation has reduced job failures by 60%.
• CA Workload Automation has improved staff productivity and problem resolution.
• CA Workload Automation has improved application availability.

Incentivized