Language Bindings Survey

We need your feedback to implement BTrDB bindings in new languages!

Benjamin Bengfort, PhD

November 06, 2019

We need your help to select the next languages to implement BTrDB bindings in! Please give us your feedback on this very short survey (it should take no more than 5 minutes):

BTrDB Analytics Language Bindings Survey.

At the core of the NI4AI PredictiveGrid platform is the BTrDB database - a timeseries database that has been designed from the ground up for dense or high volume telemetry analytics produced by sensors like syncrhophasors that monitor the grid. User access is provided to the database in two ways:

  1. Through applications like the Plotter that allow users to interact with the data.
  2. Using Jupyter Notebooks to conduct direct analyses and queries against the database.

Both of these interactions require bindings — library code that allows you to connect to and query the database — to build applications and write analytics code. Currently we have language bindings in Go and Python — langauges that are well suited to both types of interactions. However, to equip more people to access the platform we’d like to write bindings in the langauges that you use for analytics!

The survey allows you to submit your top three analytics langauges. The languages choices are those that have notebook kernels that are supported by Jupyter. If there is a language that has a notebook kernel that is missing, please let us know! The top three choices selected by our users will have langauge bindings implemented and documented throughout the course of the project.

This is one of the easiest ways to get involved with NI4AI and we’re looking forward to hearing your thoughts! Thank you!

Author

Benjamin Bengfort, PhD

Benjamin received his PhD in Computer Science at the University of Maryland studying consistency in geo-replicated distributed systems. He has over 12 years of software engineering and machine learning expertise in both the commercial and military domains and has developed multiple large-scale learning applications.