Apache Spark vs. Devart Excel Add-ins

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.

Incentivized

For a mid size company it is a perfect tool like IT application companies but if companies having large Data like Banks it is less appropriate

Incentivized

• 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

Incentivized

• Source control
• Unit testing
• Schema comparisons

Incentivized

• 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

Incentivized

• It has room for improvement in terms of security and making it possible for large organizations like us to use it in a more secure way with integrating with our in-house security systems.
• Pricing could be reduced further for it to be adopted world-wide.
• it isn't foolproof. Excel has no means of checking for human error during data entry, which means that the wrong information can skew all the results - sometimes severe.
• Users unfamiliar with Excel syntax may also find entering calculations and calling up other functions a bit frustrating until they get a solid understanding.

Incentivized

Capacity of computing data in cluster and fast speed.

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

Incentivized

It automated the whole reporting and excel. Our team can generate pivot, dashboards and multiple other reports in various formats and update them with one click. My Sales team need to update more then 1500 leads status on daily basis. By using this tool now they can do this in few minutes.

Incentivized

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.

Incentivized

Microsoft Excel is the most used tool in the software universe. As such, asking Microsoft Excel to be the tool used post data acquisition makes a ton of sense. Almost everyone we hire has some idea on how to use this tool, which decreases the time spend training new employees.

Incentivized

• 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

Incentivized

• It had a positive ROI as we used it on its strengths. We could save time and make some of our processes simpler.
• It does help us up to a certain limit - but it acts as a part of our general IT infrastructure. It cannot solve our direct business objectives.
• It cannot be expanded in all directions as we need a lot more native coding and IT infrastructure.

Incentivized