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 Ideal for daily standard ETL use cases whether the data is sourced from / transferred to the native connectors (like SQL Server) or FTP. Best if the company uses MS suite of tools. There are better options in the market for chaining tasks where you want a custom flow of executions depending on the outcome of each process or if you want advanced functionality like API connections, etc.
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 Ease of use - can be used with no prior experience in a relatively short amount of time. Flexibility - provides multiple means of accomplishing tasks to be able to support virtually any scenario. Performance - performs well with default configurations but allows the user to choose a multitude of options that can enhance performance. Resilient - supports the configuration of error handling to prevent and identify breakages. Complete suite of configurable tools. 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 SSIS has been a bit neglected by Microsoft and new features are slow in coming. When importing data from flat files and Excel workbooks, changes in the data structure will cause the extracts to fail. Workarounds do exist but are not easily implemented. If your source data structure does not change or rarely changes, this negative is relatively insignificant. While add-on third-party SSIS tools exist, there are only a small number of vendors actively supporting SSIS and license fees for production server use can be significant especially in highly-scaled environments. Read full review Likelihood to Renew Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
Read full review Usability SQL Server Integration Services is a relatively nice tool but is simply not the ETL for a global, large-scale organization. With developing requirements such as NoSQL data, cloud-based tools, and extraordinarily large databases, SSIS is no longer our tool of choice.
Read full review Performance Raw performance is great. At times, depending on the machine you are using for development, the IDE can have issues. Deploying projects is very easy and the tool set they give you to monitor jobs out of the box is decent. If you do very much with it you will have to write into your projects performance tracking though.
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 The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
Read full review Implementation Rating The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
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 I had nothing to do with the choice or install. I assume it was made because it's easy to integrate with our SQL Server environment and free. I'm not sure of any other enterprise level solution that would solve this problem, but I would likely have approached it with traditional scripting. Comparably free, but my own familiarity with trad scripts would be my final deciding factor. Perhaps with some further training on SSIS I would have a different answer.
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 Data integrity across various products allows unify certain processes inside the organization and save funds by reducing human labour factor. Automated data unification allows us plan our inputs better and reduce over-warehousing by overbuying The employee number, responsible for data management was reduced from 4 to 1 person Read full review ScreenShots