Likelihood to Recommend 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.
Read full review It helps our BUs analyze data and create dashboards they can understand. While slowing down with a large database, it becomes less helpful. In my experience, it is excellent in consolidating information from several sources for analysis, decision-making, and knowledge gaps. Excellent at managing information access.
Read full review Pros 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. Rajarshi Maitra Director/Client Engagement Leader- P&C Insurance (Digital Transformation)
Read full review It integrates well with our current ecosystem of SAP products, like HANA. It provides end-to-end machine learning operations, with tools for the complete model life cycle. It has a simple user interface for novice users, with complex tools also available for power users. It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality. It can run in the cloud, no on-premise software management needed. Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer. SAP is very actively developing and improving it. Read full review Cons 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. Read full review Data transfer speed tends to be slow when there is poor internet connection since SAP Data Intelligence don’t synchronize data while offline. However, this is not vendor fault, that’s why we have implemented robust wireless technology internet connection in our organization. Read full review Likelihood to Renew Allow collaborations among various personas with insights as ratings and comments on the datasets Reuse knowledges on the datasets for new users Next-Gen Data Management and Artificial Intelligence
Read full review Usability Good tool with lots of potential, but I still see a lot of room for improvement, e.g. when it comes to debugging functionality to understand exactly where pipelines fail and what the data at that point looks like (similar to BW debugging). Also, I am missing SAPs standard machine learning libraries (Python) to be pre-installed, among some other general usability improvements.
Read full review Support Rating 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
Read full review Initially we struggle to get help from SAP but then dedicated Dev angel was assigned to us and that simplify the overall support scenario. There is still room of improvement in documentation around SAP Data intelligence. We struggle a lot to initially understand the feature and required help around performance improvement area,
Read full review Alternatives Considered 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.
Read full review One of the reasons to pick SAP Data Intelligence is the speed and security it provides, in addition to the excellent support it provides. It is also compatible with all popular databases, which is another reason to choose it.
Read full review Return on Investment It is very useful and make things easier Debugging can improve Its better suited than other products with the same objective Read full review Automation of data management slashed tasks by over 60% in most departments for the first 8 months. Metadata catalogs have enabled us to categorize data from disjointed sources in one place. It runs multiple ML models which enhances flexibility when managing data. Read full review ScreenShots SAP Data Intelligence Screenshots