Apache Spark vs. VMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
VMware Tanzu Data Services
Score 8.4 out of 10
N/A
VMware Tanzu Data Services is a portfolio of on-demand caching, messaging, and database software on VMware Tanzu for development teams building modern applications.N/A
Pricing
Apache SparkVMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkVMware Tanzu Data Services
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
Community Pulse
Apache SparkVMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)
Considered Both Products
Apache Spark
Chose Apache Spark
We specifically choose Spark over MapReduce to make the cluster processing faster
VMware Tanzu Data Services

No answer on this topic

Top Pros
Top Cons
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Apache SparkVMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)
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No answers on this topic

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User Ratings
Apache SparkVMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)
Likelihood to Recommend
9.7
(24 ratings)
8.0
(1 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.6
(6 ratings)
8.0
(1 ratings)
User Testimonials
Apache SparkVMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)
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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VMware
If you need to execute ml algorithms, learning techniques, or mathematical calculations on large amounts of heterogeneous data, VMware Tanzu Data Services will be ideal. It will be really simple to set up, particularly if you choose AWS as your integrated cloud provider. However, if you're working with lower data amounts, such as gigabytes, it can be superfluous.
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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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VMware
  • Apache MADlib provides popular machine learning functionality.
  • Allows you to query terabytes of data databases.
  • Interoperability for AWS S3 is effortless.
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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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VMware
  • Running on Azure is a little more difficult.
  • Synchronization with Kafka may be a little easier.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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VMware
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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VMware
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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VMware
They were very helpful. We needed support for initial implementation.
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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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VMware
No answers on this topic
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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VMware
  • There was a noticeable reduction in system reliability.
  • Saw a reduction in unsuccessful analytics operations.
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