Informatica’s Intelligent Cloud Services (IICS) platform is a solution for synchronizing and integrating cloud and on-premise applications. It offers prebuilt connectors and actions between applications and programs, allowing for data transformation within the program, as well as case-specific services.
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 Cloud is a great tool for use when data must be formatted consistently. Once configured, it is very robust and reliable. It is also well-suited for an organization without a robust IT staff to maintain a full server infrastructure. It offers a cost-effective approach to high-quality data integration for even the largest organizations. Organizations without staff experienced in data analytics may find it challenging to take advantage of the more complex results of this tool.
Once the secure connection is established it’s quite easy to operate and create new jobs. The controls are simple, and we appreciate the fact there are not a lot of complex fine-tunings required. Navigation is also easy, and we enjoy the ability to open multiple tabs in the browser to work on multiple projects.
The monitoring functionality works well to help track the progress of the jobs, again, without too much complication. In a fast dev environment, speed is essential and we quickly seeing the status/progress of jobs as well as any errors if the jobs fail helps us maintain speed.
The web interface is a lot easier to interact with than the client/on-prem version. Putting much of the heavy lifting of interacting with the tool onto the shoulders of the browser makes it easier to keep multiple sessions open and get in/out quickly without having to VPN into the office.
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've never had trouble getting into contact with Informatica's support for technical help. I give it a nine because it does pretty well for mid to enterprise-scale workflows.
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
First, the wizard is easy to use making the learning curve for simple ETL tasks nice. Second, since Informatica is mature there are a good variety of connectors available. Finally, we have driven some fairly complex ETL solutions using only the cloud.
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.
