Azure Data Factory vs. Salesforce Data Cloud

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

I would recommend Salesforce Data Cloud as an 8 because it is great for keeping track of customer and sale information, it is easy to use, it is easy to generate reports and it is user friendly. However, it is very expensive and does not integrate with other CRMs well

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

• Integration with other systems is really working for us. Also having models developed in Data Cloud and then use for servers, Salesforce sales and for Marketing Cloud.

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• 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.

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• Sometimes it can be very fidgety and you need a team of salesforce developers in order to implement the solution
• Can be expensive, but so are other providers

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

Because it is worth this, the analytics, reporting, CRM, security parameters, mobile support all make it this worth. Also, I personally believe it has high potential to grab customers attention and increase sales.

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

I like Salesforce Data Cloud because like mentioned before, it is easy to use, easy to generate reports, create workflows, keep track of customer data, sales data, house data, etc. You can also import excel sheets with customer data as well and it will create the fields for you which is nice

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

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• It's been really good because for example, now we can use some of the segmentation of our clients. We have all the models running in that cloud, so it's been easier for us having this information built and having the calculation of the segments that we want to use with clients.

Incentivized