Relevant application areas

  1. E-commerce
  2. Payment and Banking
  3. Ridesharing and cabs on hire (for example: Uber, OLA etc.)
  4. Home delivery entities (for example: food etc.)


There are several challenges here, some of them could be :

  1. Volume and Velocity.The number of messages could be very high, as these could be several users sending messages per second across the geographical areas. Hence data ingestion in real time is critical.
  2. The messages could be in English or in other vernacular langauages, hence we need to extract sentiment from unstructured data, and keep improving or updating the models in real time.
  3. Extracting patterns from the streaming set of events in continuous manner, this requires CEP on the streaming data which is very hard to implement on SQL or regular NoSQL databases.
  4. Storing certain truples (sub, obj, predicate) in a graph which is continously updated as events arrive, helpful in linking data and/or events.
  5. Different database queries along with text search which requires many secondary and reverse indexes.
  6. Infrstructure deplyment and maintenance if too many siols are used. Further automation is difficult to achieve in typical deployment models.

Benefits of using BangDB

  1. Use light weight high performance bangdb agents or other messaging framework to stream data into the BangDB. BangDB is high performance database with ingestion speed over 5K+ events per second per server leading to half a billion events processing per commodity server in a day.
  2. Integrated stream processing within BangDB allows users to simply start the process with a simple json schema definition. There is no extra silos setup for streaming infrastructure.
  3. Ingerated AI within BangDB allows users to simply train, deploy and predict on incoming data without having to set up separate infra and then exporting data/ importing model etc. The entire process can be automated within BangDB.
  4. BangDB is a multi model database and it also allows Graph to be integrated with streams such that the graph is updated on streaming data with triples.
  5. BangDB supports many kinds of indexes including reverse indexes, hence running rich queries along with searches on BangDB is quite simple.
  6. Integrated with Grafana for visualization of time-series data.

Overview of the solution

Checkout the details here.

Please let us know if you want more features in the use case to be added or another use case to be implemented and shared. Also do send me your feedback / comment at