Apache Spark vs. IBM Cloud Pak for Data

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.

Incentivized

Unlike others analytics tool IBM Cloud Pak for Data provides out-of-the-box privacy, model interpretability and fairness monitoring, along with automatic explanation of data and models written in business language. It's a great tool that all business should emulate. Great user experience because of every feature is functional and improved constantly.

Incentivized

• It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
• It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
• Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.

Incentivized

• The AI services catalog like the Watson Assistant is very good.
• The analytics dashboard featuring all the recent history is very good with IBM.
• Searching for data through the unified search option is super cool.

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

• It is complex; The platform comes with several features from MLOPs to Customer 360 which may take a long time for the user to understand.

Incentivized

Capacity of computing data in cluster and fast speed.

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

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.

We used Surprise Kit for one of the other research works. It is more fine-tuned to Recommendation systems and their algorithms. Apache Spark has MLlib for majority of ML problems. Where as software like Surprse Kit - it suitable for a specific task of Recommendations only

Incentivized

IBM has healing mechanisms when resource usage is high. This platform performs well, but when it runs out of capacity, it has crashed for many clients. This is innate in its original design

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

• can improve readiness for cloud migration, improve licensing flexibility with IBM, and reduce both hardware purchases and infrastructure management efforts.
• reduces the expenses of internal resources.
• should improve efficiencies, reduce risks, and increase performance

Incentivized