Apache Spark vs. Devart Excel Add-ins

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 9.0 out of 10
N/A
N/AN/A
Devart Excel Add-ins
Score 9.1 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.
$399.95
one-time fee
Pricing
Apache SparkDevart Excel Add-ins
Editions & Modules
No answers on this topic
Excel Add-in Database Pack
$399.95
one-time fee
Excel Add-in Cloud Pack
$499.95
one-time fee
Excel Add-in Universal Pack
$599.95
one-time fee
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—Purchases include a perpetual license and 1 year of subscription which includes the product updates and premium support.
More Pricing Information
Community Pulse
Apache SparkDevart Excel Add-ins
Best Alternatives
Apache SparkDevart Excel Add-ins
Small Businesses

No answers on this topic

Skyvia
Skyvia
Score 10.0 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 7.6 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkDevart Excel Add-ins
Likelihood to Recommend
9.2
(24 ratings)
8.0
(2 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
8.4
(4 ratings)
-
(0 ratings)
Support Rating
8.7
(4 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.
Read full review
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.
Read full review
Pros
Apache
  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
Read full review
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.
Read full review
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
Read full review
Devart
  • The data pump feature breaks sometimes
  • The profile usage monitoring is slow
  • The event profiler is delayed
Read full review
Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
Read full review
Devart
No answers on this topic
Usability
Apache
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
Read full review
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.
Read full review
Devart
No answers on this topic
Alternatives Considered
Apache
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
Read full review
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.
Read full review
Return on Investment
Apache
  • Business leaders are able to take data driven decisions
  • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
  • Business is able come up with new product ideas
Read full review
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
Read full review
ScreenShots

Devart Excel Add-ins Screenshots

Screenshot of Screenshot of Screenshot of Screenshot of