Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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Superhuman
Score 9.0 out of 10
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Superhuman for Business is an email management solution that helps to enhance team collaboration and help users maintain focus. Some features include timed sent, Unsend, conversation snooze, support for offline actions, Gmail shortcuts, etc.
$30
per month per user
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Apache Spark
Superhuman
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$30
per month per user
Business
$40
per month per user
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Apache Spark
Superhuman
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Up to 18% discount for annual billing on Starter and Business plans.
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.
1. Inbox management 2. Saving time 3. For CXOs, it can save a lot of time. 4. Superhuman AI is very good, optimized for the use case. 5. UI/UX is to notch, the team has really put in some effort here. 6. Although there's a learning curve but the onboarding makes it very simple.
When I started adopting it, it was VERY Apple-focused. I could not get access to Android and Windows. They are not releasing an Android version, and there is web access for Windows, which I use with an iPhone.
Built-in calendar, while nice, could be a little more functional.
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.
