Apache Spark vs. IBM Streams

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.

Incentivized

Like the name says, it is good for streaming data and analyzing. It is great to look at tuples at a fast rate, filtering, calling other sources to enrich data, can call APIs, etc. Could do better for ingest use cases, can do better with guaranteed delivery, etc.

Incentivized

• Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
• Apache Spark does a fairly good job implementing machine learning models for larger data sets.
• Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.

Incentivized

• IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
• Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
• Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
• In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.

Incentivized

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

• Documentation could be more extensive, with more examples, although overall this is not too bad compared to some of the alternative solutions.
• Seems expensive to use in production.

Incentivized

Capacity of computing data in cluster and fast speed.

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.

Incentivized

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

Incentivized

There are well explained tutorials to get the user started. If you are looking for business application ideas, the user community offers a diversity of applications. It is very easy to launch applications on the cloud and can integrate with other analytic tools available on Watson Studio. It takes away the burden of the technology so that users can focus on business innovations.

Incentivized

• 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.

Incentivized

• Ability to do more with less
• Admins and data analyst can now focus on more thinking tasks
• No negative impacts yet

Incentivized