Apache Spark Pros and Cons

I feel the streaming is its best feature.

AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI.

The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly.

I found the solution stable. We haven't had any problems with it.

The most valuable feature of this solution is its capacity for processing large amounts of data.

The features we find most valuable are the machine learning, data learning, and Spark Analytics.

The main feature that we find valuable is that it is very fast.

The scalability has been the most valuable aspect of the solution.

The solution is very stable.

The processing time is very much improved over the data warehouse solution that we were using.

When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources.

Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing.

The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate.

It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.

When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.

We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.

We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.

The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.

The solution needs to optimize shuffling between workers.

I would like to see integration with data science platforms to optimize the processing capability for these tasks.