Apache Spark vs. IBM Cloud Pak for Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
IBM Cloud Pak for Data
Score 8.8 out of 10
N/A
IBM Cloud Pak for Data (formerly IBM Cloud Private for Data) provides data management, data governance, and automated data discovery and classification.N/A
Pricing
Apache SparkIBM Cloud Pak for Data
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkIBM Cloud Pak for Data
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Best Alternatives
Apache SparkIBM Cloud Pak for Data
Small Businesses

No answers on this topic

Egnyte
Egnyte
Score 7.5 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
ER/Studio Data Architect
ER/Studio Data Architect
Score 9.9 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
ER/Studio Data Architect
ER/Studio Data Architect
Score 9.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkIBM Cloud Pak for Data
Likelihood to Recommend
9.9
(24 ratings)
9.0
(14 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.1
(2 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
-
(0 ratings)
User Testimonials
Apache SparkIBM Cloud Pak for Data
Likelihood to Recommend
Apache
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.
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IBM
IBM Cloud Pak for Data with Netezza is well suited for clients who require fast, economical analytics processing. It is not designed to be used as a transactional processing environment. For example, a large customer is using it during the point of sale process. That makes little sense in that business case. However, to take analysis to market faster, it excels well in that space.
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Pros
Apache
  • 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.
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IBM
  • I really like the AI and ML which enables us to source data in different sources for easy data-driven decisions.
  • It's a cloud tool that keeps all our data safe, backed up ahs obtainable at any time without being exposed to any kind of risks or loss.
  • I like the fact that ICP is main based on open source stack which adds value to products like VA or MCM.
  • IBM support service is great and top-class.
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Cons
Apache
  • 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
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IBM
  • Basically I feel price is not very pocket friendly especially for the small business organisation. Should introduce flexible pricing plans.
  • The templates can sometimes be to cumbersome to customize and there are more manual steps when it comes to down time.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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IBM
No answers on this topic
Usability
Apache
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.
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IBM
No answers on this topic
Support Rating
Apache
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.
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IBM
No answers on this topic
Alternatives Considered
Apache
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
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IBM
Generally this tool has been very helpful and innovative because increase our workflow and collaboration using integrated multi-cloud platform. It also enables us to deploy in any flexible way like on-premises or cloud which saves time and hard disk space. It also enables us to connect, catalog, govern, transform and analyze data regardless of the area.
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Return on Investment
Apache
  • 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.
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IBM
  • IBM Cloud helps us to manage data speed across every distributed stores and clouds.
  • Acts as a single unified tool which brings all our data in one place where it's safe and easy to access.
  • Enables all of our data users to collaborate from a single, unified interface that supports many services that are designed to work seamless.
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