Hex.tech vs. pandas

Hex.tech is an excellent tool for a product intelligence or product analytics team that wants to enable widespread use of available data for decision-making. It requires some SQL experience in many cases but makes it possible to go far without complex querying skills.

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Pandas are great for quick and relatively simple analytics and visualizations

Pandas work well for exploratory ad-hoc analytic work

But , We had little success in implementing complicated predictive analytics. And large data sizes can be a problem.

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• Powerful interactive notebook environment
• Balances the need of code and non-code users
• Provides free and easy sharing to viewers of analyses across the company
• Responds promptly to support requests and needs
• High quality native charting capabilities

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• It is easy to do statistical analysis
• It is easy to clean the data
• It is easy to produce graphs and charts to visualize

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• Limited support for R notebooks
• Limited sharing outside of the app (e.g., no slack integration)
• Some Python knowledge needed for highest levels of aggregations

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• There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
• Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
• In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case

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Over the years, we tried a lot of different frameworks and tools, homegrown and commercial. Pandas provide the best results.

It is lightweight, flexible and easy to implement.

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Hex.tech is the easiest to use, most enjoyable experience, most powerful environment, and most cost scalable of all the above.

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All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.

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Independently Invited by TrustRadius

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• Increased visibility into analyses with free membership tier across company
• Increased capacity for self-service analytics among R&D stakeholders (e.g., product managers)
• Improved productivity of data science team with ease of use

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
• Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info