Cloud deployment


Cloud deployments have following advantages:

  • High hardware, data centre and network reliability
  • Fast scale-out and scale-up:
    • For example there are is no lead-time and no rack-space, power and cooling limits typical of on-premises deployment
    • Its possible to access the largest machines with no lead time (e.g., 16TB RAM, 128 (hyper-)thread / 64 core CPUs )
  • Fast scale-down: short-term projects consume resources in proportion to their duration
  • Maintained, scalable cloud-native services:
    • Databases
    • File and object storage
    • Task distribution

Cloud deployment however comes with some investment in learning, potential for higher operational expenditure and some risk of vendor-lockin.

We are able to provide advice and license specific software components to make it faster and cheaper to deploy analytics services into cloud and cloud-like environments. We have experience with Microsoft Azure and Amazon AWS.

Our experience

We have experience in following areas:

  • Integration of QuantLib analytics with Azure Synapse Analytics and AWS EMR. This enables scalable, low-maintenance, stable and data-driven analytics suitable for enterprise deployment.

  • Deployment and optimisation of Python Jupyter notebooks and integration with cloud-native service

  • Deployment and integration of QuantLib Java-based bindings with cloud-native services

  • Integration of QuantLib with AWS Lambda functions for fastest scale-out and scale-down

Copyright and published by: BN Algorihtms Ltd 2023. For general information only. Not to be relied for any purpose. Not advice about investment. No warranty of any kind. No liability for any use of this information accepted. Contact: