Apache Spark Pros and Cons

I feel the streaming is its best feature.

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.

Features include machine learning, real time streaming, and data processing.

The fault tolerant feature is provided.

It provides a scalable machine learning library.

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.

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.

It should support more programming languages.

Needs to provide an internal schedule to schedule spark jobs with monitoring capability.