3 ms·

Show HN: Quickly and easily analyze Covid death data (or any other data)

3 points by didgetmaster 4y ago

didgetmaster 4y ago
The DidgetBrowser is a Windows application that is easily downloaded from <www.Didgets.com> and used to build relational tables quickly and perform queries in record time. The speed and ease of use makes it good for analyzing large data sets. The Windows app is currently in Beta. The code is written to be cross-platform but the Linux and MacOS versions are not yet available.
- peteradio 4y ago
  > The code is written to be cross-platform but the Linux and MacOS versions are not yet available. Could you help me understand how I could help here? Perhaps I could help bridge the linux gap?
  - didgetmaster 4y ago
    It is mainly a resource problem. Didgets is a small startup with limited resources. We are having an open beta to try and draw some interest and gain some traction in the market. I have actually built and tested it on Linux before, but I just don't have the bandwidth to do that with every build for various Linux distros.
m348e912 4y ago
As the headline implies, this is less about covid data but rather a tool to quickly analyze a large dataset. It looks snappy and very useful (and somewhat no frills as well)
- didgetmaster 4y ago
  Thanks. If you have interest there are some more videos on my youtube channel that show some of its other capabilities. https://www.youtube.com/channel/UC-L1oTcH0ocMXShifCt4JQQ https://www.youtube.com/channel/UC-L1oTcH0ocMXShifCt4JQQ
  - m348e912 4y ago
    I checked out a few videos earlier today. I think you did a great job with the product. I hope you find an audience and do well. Are you a team of one or do you have additional developers?
    - didgetmaster 4y ago
      I have a few friends who have helped me with parts of it (mostly with the UI), but I have done about 98% of the work. It has been mostly a hobby project for me. I wanted to see how fast I could make certain data operations using threading techniques on multi-core CPUs. (I wrote the first line of code on a Core-2 Duo machine.) I came up with some novel ways to structure the data for easy storage and manipulation as well as some really efficient algorithms. I am able to break up single queries to a table and process parts of them in parallel. This results in some really fast response times (a bunch of queries tested on my system vs Postgres v13 are about 10x faster).

3 points by didgetmaster 4y ago

The DidgetBrowser is a Windows application that is easily downloaded from <www.Didgets.com> and used to build relational tables quickly and perform queries in record time. The speed and ease of use makes it good for analyzing large data sets. The Windows app is currently in Beta. The code is written to be cross-platform but the Linux and MacOS versions are not yet available.

peteradio 4y ago
> The code is written to be cross-platform but the Linux and MacOS versions are not yet available. Could you help me understand how I could help here? Perhaps I could help bridge the linux gap?
- didgetmaster 4y ago
  It is mainly a resource problem. Didgets is a small startup with limited resources. We are having an open beta to try and draw some interest and gain some traction in the market. I have actually built and tested it on Linux before, but I just don't have the bandwidth to do that with every build for various Linux distros.

m348e912 4y ago

As the headline implies, this is less about covid data but rather a tool to quickly analyze a large dataset. It looks snappy and very useful (and somewhat no frills as well)

didgetmaster 4y ago
Thanks. If you have interest there are some more videos on my youtube channel that show some of its other capabilities. https://www.youtube.com/channel/UC-L1oTcH0ocMXShifCt4JQQ https://www.youtube.com/channel/UC-L1oTcH0ocMXShifCt4JQQ
- m348e912 4y ago
  I checked out a few videos earlier today. I think you did a great job with the product. I hope you find an audience and do well. Are you a team of one or do you have additional developers?
  - didgetmaster 4y ago
    I have a few friends who have helped me with parts of it (mostly with the UI), but I have done about 98% of the work. It has been mostly a hobby project for me. I wanted to see how fast I could make certain data operations using threading techniques on multi-core CPUs. (I wrote the first line of code on a Core-2 Duo machine.) I came up with some novel ways to structure the data for easy storage and manipulation as well as some really efficient algorithms. I am able to break up single queries to a table and process parts of them in parallel. This results in some really fast response times (a bunch of queries tested on my system vs Postgres v13 are about 10x faster).