SAP® Adaptive Server® Enterprise (SAP ASE) was a solution that handled massive volumes of data and thousands of concurrent users to accelerate the growth of new data-driven business applications. SAP ASE is a legacy product. It is end of sale (EOS), and reached End of Mainstream Maintenance December 2020.
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.
High-performance, high-concurrency transactions are well suited for ASE. ASE is lacking some features in my opinion such as history tables, however there are ways to implement them via workarounds or by using Replication Server. I do think the way the ASE parser and optimizer works are far superior to other products as it is a true cost-based optimizer and the order of the tables in the FROM clause does not really matter although a good SQL coder will place the tables in a meaningful order to make the SQL more readable. ASE is good for applications that require high availability and can be used for mission critical systems.
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.
It does almost everything we need and for the things it doesn't do natively, we are still able to do using other features. For example, natively history tables weren't supported but we were able to create them using triggers.
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.
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
Much less effort than Oracle. Much better customer support than Oracle. Roughly equivalent to SQL Server in performance and ease of use. Much better customer service than SQL Server. Different ballpark from IQ. Same customer service.
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.
