Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.
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Informatica PowerCenter (legacy)
Score 9.1 out of 10
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Informatica PowerCenter was data integration technology designed to form the foundation for data integration initiatives, application migration, or analytics. It is a legacy product.
Azure Data Factory is more of a universal pipeline. SAP BW is a tool offering good SAP connectivity but very limited third-party connectivity. The same is the case with BW4hana. SAP Datasphere is offering better connectivity with SAP sources, but not so good when compared to …
Azure Data Factory helps us automate to schedule jobs as per customer demands to make ETL triggers when the need arises. Anyone can define the workflow with the Azure Data Factory UI designer tool and easily test the systems. It helped us automate the same workflow with …
Azure Data Factory is a relatively new player in the space, and its feature set marks it as such. It does not have the full features of a more mature product set such as any of the above. However, it does allow for the creation of ETL/ELT flows/pipelines with minimal initial …
Best scenario is for ETL process. The flexibility and connectivity is outstanding. For our environment, SAP data connectivity with Azure Data Factory offers very limited features compared to SAP Data Sphere. Due to the limited modelling capacity of the tool, we use Databricks for data modelling and cleaning. Usage of multiple tools could have been avoided if adf has modelling capabilities.
1.- Scenaries with poor sources of data is not recomended (Very bad ROI). The solution is for medium-big enterprises with a lot of sources of data and users. 2.- Bank and finance enviroment to integrate differente data form trading, Regulatory reports, decisions makers, fraud and financial crimes because in this kind of scenary the quality of data is the base of the business. 3.- Departments of development and test of applications in enterprises because you can design enviroments, out of the production systems, to development and test the new API's or updateds made.
Informatica Powercenter is an innovative software that works with ETL-type data integration. Connectivity to almost all the database systems.
Great documentation and customer support.
It has a various solution to address data quality issues. data masking, data virtualization. It has various supporting tools or MDM, IDQ, Analyst, BigData which can be used to analyze data and correct it.
Granularity of Errors: Sometimes, Azure Data Factory provides error messages that are too generic or vague for us, making it challenging to pinpoint the exact cause of a pipeline failure. Enhanced error messages with more actionable details would greatly assist us as users in debugging their pipelines.
Pipeline Design UI: In my experience, the visual interface for designing pipelines, especially when dealing with complex workflows or numerous activities, can become cluttered. I think a more intuitive and scalable design interface would improve usability. In my opinion, features like zoom, better alignment tools, or grouping capabilities could make managing intricate designs more manageable.
Native Support: While Azure Data Factory does support incremental data loads, in my experience, the setup can be somewhat manual and complex. I think native and more straightforward support for Change Data Capture, especially from popular databases, would simplify the process of capturing and processing only the changed data, making regular data updates more efficient
There are too many ways to perform the same or similar functions which in turn makes it challenging to trace what a workflow is doing and at which point (ex. sessions can be designed as static or re-usable and the override can occur at the session or workflow, or both which can be counter productive and confusing when troubleshooting).
The power in structured design is a double edged sword. Simple tasks for a POC can become cumbersome. Ex. if you want to move some data to test a process, you first have to create your sources by importing them which means an ODBC connection or similar will need to be configured, you in turn have to develop your targets and all of the essential building blocks before being able to begin actual development. While I am on sources and targets, I think of a table definition as just that and find it counter intuitive to have to design a table as both a source and target and manage them as different objects. It would be more intuitive to have a table definition and its source/target properties defined by where you drag and drop it in the mapping.
There are no checkpoints or data viewer type functions without designing an entire mapping and workflow. If you would like to simply run a job up to a point and check the throughput, an entire mapping needs to be completed and you would workaround this by creating a flat file target.
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.
Positives; - Multi User Development Environment - Speed of transformation - Seamless integration between other Informatica products. Negatives; - There should be less windows to maintain developers' focus while using. You probably need 2 big monitors when you start development with Informatica Power Center. - Oracle Analytical functions should be natively used. - E-LT support as well as ETL support.
PowerCenter is robust and fast, and it does a great job meeting all the needs, not just the most commercially vocal needs. In the hands of an expert power user, you can accomplish almost anything with your data. It is not for new users or intermittent users-- for that the Cloud version is a better fit. Be prepared for costly connectors (priced differently for each source or destination you are working with), and just be planful of your projects so you are not paying for connectors you no longer need or want
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
Informatica power center is a leader of the pack of ETL tools and has some great abilities that make it stand out from other ETL tools. It has been a great partner to its clients over a long time so it's definitely dependable. With all the great things about Informatica, it has a bit of tech burden that should be addressed to make it more nimble, reduce the learning curve for new developers, provide better connectivity with visualization tools.
Azure Data Factory helps us automate to schedule jobs as per customer demands to make ETL triggers when the need arises. Anyone can define the workflow with the Azure Data Factory UI designer tool and easily test the systems. It helped us automate the same workflow with programming languages like Python or automation tools like ansible. Numerous options for connectivity be it a database or storage account helps us move data transfer to the cloud or on-premise systems.
While Talend offers a much more comfortable interface to work with, Informatica's forte is performance. And on that front, Informatica Enterprise Data Integration certainly leaves Talend in the dust. For a more back-end-centric use case, Informatica is certainly the ETL tool of choice. On the other hand, if business users would be using the tool, then Talend would be the preferred tool.
The data pipeline automation capability of Informatica means that few resources are needed to pre-process the data that ultimately resides in a Data Warehouse. Once a workflow is implemented, manual intervention is not needed.
PowerCenter did require more resources and time for installation and configuration than was expected/planned for.
The lack of or minimal support of unstructured data means that newer sources of dynamic/changing data cannot be easily processed/transformed through PowerCenter workflows.
