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 Spark
Devart Excel Add-ins
Editions & Modules
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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 Spark
Devart Excel Add-ins
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Purchases include a perpetual license and 1 year of subscription which includes the product updates and premium support.
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.
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.
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
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.
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.
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.
