Document Management Solution
In 2015 KnubiSoft was approached by a Healthcare Services Provider from the UK with a request to help them with their existing software product. The product was a document storage solution for private groups in a healthcare domain. It allows healthcare professionals, patients, and other users to create private documents online, manage them and share in a secure way.
Python 3.5, Django, Google App Engine (MySQL), Amazon S3
Dedicated Team, Business Process Management, Enterprise System Transformation, UX Consulting
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Our client is a prominent software development services provider from the UK focused on clients from the Healthcare domain.
When the client approached us with a request to help him rebuild the project, it had already existed for over 4 years. One of their anchor products was a documents management software, and the client was unhappy with its current performance.
The application was developed in Python 2.7, the codebase was poorly-structured and almost not documented, so the app was slow, often crashed and required immense efforts on testing when even the smallest functionality was added or changed. In addition, the software didn’t 100% comply with the security requirements mandatory for healthcare software. Also, they were using the database incorrectly and were making SQL queries which were not optimized. The expensive Google App Engine servers were eating a lot of costs.
We have analyzed the database and source code of the current solution. Then we have analyzed and documented on high-level all functional and non-functional requirements towards the updated software.
We have automated load testing, functional testing and implemented regression tests which immediately reduced load on QA team and allowed the client to avoid shipping code with critical errors. Also, we have moved the project to a new scalable database in the cloud which addressed many performance bottle-necks.
We implemented better Google App Engine ecosystem: we changed the architecture to micro-services approach, which allows building new physically isolated components that do not affect each other and in case some component is down or failed, all other services are still available for the client.
We added Load Balancing and Automatic Scalability, which allows the client to use cloud server efficiently and pay only for the capacity that is required. We have also set up DataDog for real-time application performance monitoring and crash notiöcations (www.datadoghq.com).
Completely rewritten the project using the latest Python 3.5 added Django support (Python framework for easier development) significantly enhanced security using Django tools (http://bit.ly/2gJskY4) added site content caching to improve product performance speed changed & fixed multiple bugs to remove flow blockers moved documents storage to Amazon Simple Storage Service (S3).
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