Apache Spark vs. Devart Excel Add-ins

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Devart Excel Add-ins
Score 7.9 out of 10
N/A
Devart Excel Add-ins allow you to use Excel capabilities to import, process, and analyze data from cloud applications and relational databases. The Excel Add-ins also allow users to make data changes and then save those changes back to the data source they were originally imported from.
$119.95
Pricing
Apache SparkDevart Excel Add-ins
Editions & Modules
No answers on this topic
Standard Edition
$119.95
Offerings
Pricing Offerings
Apache SparkDevart Excel Add-ins
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
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Apache SparkDevart Excel Add-ins
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No answers on this topic

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Score 9.5 out of 10
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Cloudera Manager
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Score 9.7 out of 10
InfoSphere
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Score 10.0 out of 10
Enterprises
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Score 9.3 out of 10
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Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkDevart Excel Add-ins
Likelihood to Recommend
9.7
(24 ratings)
8.0
(2 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.6
(6 ratings)
-
(0 ratings)
User Testimonials
Apache SparkDevart Excel Add-ins
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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Devart
Devart Excel Add-ins is really good for data comparison of one database table against another database table. There are plenty of times where the business users need help understanding with their Power BI data models are not connecting correctly and I use this tool to break down the differences between the tables they are trying to connect.
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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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Devart
  • Devart Excel Add-in supports connectivity with various databases - which really helps to customize and massage data.
  • Since data is available in cloud, it is easily accessible everywhere.
  • It can also be integrated with SSIS components making it possible to resolve complex problems.
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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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Devart
  • The data pump feature breaks sometimes
  • The profile usage monitoring is slow
  • The event profiler is delayed
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Devart
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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Devart
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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Devart
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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Devart
Devart Excel stacks up very well against its alternatives. It has significant advantages as it keeps getting an update. Features are very much suitable for smaller organizations. For larger organizations the features cannot be used directly because of the size of the company and more native requirements of IT software requirements.
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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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Devart
  • The data compare option saves our dev team a lot of time
  • The documenter feature is nice to use for explaining table definitions
  • The event profiler is great for usage metrics
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ScreenShots

Devart Excel Add-ins Screenshots

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