Azure Data Factory vs. HCL DataConnect

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

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

• 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

• 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

• 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

• 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

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

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

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

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

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