IBM® DataStage® is a data integration tool that helps users to design, develop and run jobs that move and transform data. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load and transform (ELT) patterns. A basic version of the software is available for on-premises deployment, and the cloud-based DataStage for IBM Cloud Pak® for Data offers automated integration capabilities in a hybrid or multicloud environment.
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IBM Watson Studio
Score 10.0 out of 10
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IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.
DataStage offers better integration capabilities without the need to write code manually. It also has a native ETL engine whereas MSIS requires a SQL Server. It has better integration capabilities with data quality, data profiling and data governance tools. The main drawback of …
Excellent Cloud data mapping tool and easy creating multiple project data analytics in real-time and the report distribution are excellent via this IBM product. Easy tool to provide data visualization and the integration is effective and helpful to migrating huge amounts of data across other platforms and different websites insights gathering.
It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
Because it is a flexible tool that can manage many flows and create a strong solution with a interesting use of variables. Easy to scale up as you can copy jobs arleady build and modify them. SQL queries allow to be fast in development and have the pushdown feature, but you loose a little of user friendly look. Metadata management is not strong as a visual feature, but can be determine by job codes.
It could load thousands of records in seconds. But in the Parallel version, you need to understand how to particionate the data. If you use the algorithms erroneously, or the functionalities that it gives for the parsing of data, the performance can fall drastically, even with few records. It is necessary to have people with experience to be able to determine which algorithm to use and understand why.
IBM offers different levels of support but in my experience being and IBM shop helps to get direct support from more knowledgeable technicians from IBM. Not sure on the cost of having this kind of support, but I know there's also general support and community blogs and websites on the Internet make it easy to troubleshoot issues whenever there's need for that.
I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
No, it wasn’t my decision to use such an ETL product. I’m just the administrator at this point. I’ve heard there are other products there that are even on cloud support. That is much easier to use, more agile, and user-friendly. That doesn’t have that barrier from user to administrator to the developer standpoint.
The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
Not directly related to ROI or cost figures. Only comment here is that IBM tools tend to be more costly than average ETL tools, but it depends on if the company is an IBM shop.
One positive aspect is the company has had not a need to switch ETL tool for years.
Upgrading to newer versions of the tool brings flexibility in the tool and up-to-date features in relation to other applications.