Informatica MDM is an enterprise master data management solution that competes directly with IBM's InfoSphere and Oracle's Siebel UCM product.Informatica MDM and the company's 360 applications present a multidomain solution with flexibility to support any master data domain and relationship—whether on-premises, in the cloud, or both.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
Informatica MDM is a complete MDM solution, from ingestion to data exposition. This tool helps us in gathering customer data, and also it makes it possible for us to support our customers relationships and build customer-related strategies to improve their experience which helps to drive sales geometry and growth and customers satisfaction. On the other hand of price is relatively competitive.
This program raises us to a professional level where we have better versatility to control all the media of my work and have a correct response for each scenario.
It is essential to be right about the destination and development of my data, Informatica MDM is here to simplify all these processes for its users.
It is unfortunate how this program has a couple of limitations in terms of insertions; it does not have the ability to agglomerate and archive the data in real-time by groups.
To have automation functions, the program is very limited in performing one task at a time, compared to other systems that perform functions simultaneously.
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
I'm not sure since I never used support. My colleagues never had any issues with it, therefore my rating would be an 8 with a certain range of uncertainty.
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
Informatica MDM has proven it's worth in the organization by driving the revenue growth. It saves our lot of time by filtering out duplicate values and helps in solving critical business problems. It is very helpful when we deal with a lot of data. Apart from this we can populate data on various third party integration which is most useful case
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
I cannot speak to this for 2 reasons. 1. I am not privy to the financials associated with this implementation or the previous one. 2. We have not hit our 'go-live' for this implementation yet to compare it's performance to our previous solution.
