Complex transformations can easily be achieved using PowerCenter, which has all the features and tools to establish a real data governance strategy. Additionally, PowerCenter is able to manage huge quantities of data with a high level of reliability. We also like the metadata repository and the data warehouse application console.
We would like to see the licensing improved, though. We would like to see a feature-wise option as opposed to the current bundle option. PowerCenter is a very expensive solution. The numerous levels of transformation and multiple interfaces can make it a very confusing solution to learn.
Cloud Data Integration offers great options for data cleansing, data mastering, data masking, master data management, and API management. The UI is very easy to use and the setup is straightforward. The new licensing is very flexible. Cloud Data Integration has inbuilt tools that allow for a lot of data transformation without changing any SQL queries from the code. This is one of our favorite features.
We would point out, though, that there are some limitations with performance using Cloud Data Integration. We experienced some connectivity issues, making things run a bit slow. There are some issues with data residency laws in some countries. More regions should be added. Finally, it would behoove Cloud DataIntegration to consider adding more data sources.
These are two great solutions under Informatica Brand which is synonymous with quality, reliability, and price. (Very high price.)
Cloud Data is best suited for small to medium organizations and is somewhat limited in the regions it can be used.
PowerCenter is most applicable for large enterprises that can more easily absorb the cost and best utilize everything PowerCenter has to offer with their current bundle licensing tier.
Doing Python on exceptionally small tasks makes me admire the dynamically typed nature of this language (no want for assertion code to hold track of types), which regularly makes for a faster and less painful development system alongside the manner. but, I experience that during a whole lot larger tasks this may virtually be a hindrance, because the code might run slower than say, its equal in ...
Software Architect at a tech consulting company with 51-200 employees
13 July 17
From my own experience, your question does not admit a "one size fits all" kind of answer, in fact, the answer should start with the (annoying): it depends.
I will start with describing how I work with the kinds of tools that you mention:
When I have to solve a new problem I start exploring the problem and prototyping the solution with Python and the usual toolset (NumPy, Pandas, SciPy, MathPlotLib, the works).
If the problem is an ongoing concern, instead of a being a "just one shot" nuisance, once I have a stable prototype, but need a more robust and performant solution for the long run, I start a new project to design such a solution with .NET (typically, with C#), either on Windows or on Linux, depending on the circunstances of the problem.
So if you face similar scenarios, that is, you need to have robust and performant solutions for the long run on many of your problems, my suggestion is to start getting friendly with a compiled language like C++ or C#, or some other, and follow an approach in the line of what I do.
Kind regards, GEN