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.
It is suitable for companies without a proper data warehouse. He does very well in sales analysis and KPI management. It builds mini data warehouses, is good at data fusion, and interfaces well with other systems. Also, the export function and filter can greatly help you to get only the information you want in the format you want.
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.
We can easily provide the information that the user wants and customize it according to their needs. Sometimes a certain report can be used as the basis for creating another one that saves you time to deliver critical information in the shortest amount of time with the best results. Builds mini data warehouses, is good at data fusion, and interfaces well with other systems.
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 love how easy it is to create prototypes due to its simple simulation and modeling system. Other than that, the codes are usually simple and not very complex and it's built-in debugging adds to that ease. is an excellent tool for analyzing, classifying, and visualizing data. I do this most of the time to help me grab huge collections of data.
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
You can have a good reading of the data, you undoubtedly have cost savings and eliminate unnecessary and repetitive processes, we have unstructured data that, when structured, are elements of information that have become a competitive advantage for our organization, it is undoubtedly a strategic ally for the organization in the decision-making process
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.
