Data-driven microservice platform for African agricultural resilience
Amini is a purpose-built data infrastructure that addresses the lack of environmental information in Africa. In order to promote economic inclusion for farmers and supply chain resilience across Africa and beyond, they have built a comprehensive solution that collects, measures, and processes geospatial and soil parameters, such as soil moisture, air temperature, weather conditions, filtered satellite images, NDVI and NDII indexes, etc., to conclude if the land owner qualifies for insurance benefits in case of environmental factors that affected the faulty harvest or lack thereof.
Our challenge was to prepare an intelligent and scalable end-to-end system from scratch that could effectively process and integrate loads of data inputs, utilising DRF, FastAPI, and blockchain technologies in the form of a microservice for farmers and insurers.
The exponential data load posed a challenge to adjusting the architecture and fitting it to queue the data processing in the most efficient manner.
We supplement the client’s team with the missing skills and competencies based on the project’s needs (2 Backend Developers, 1 Front-end Developer and 1 DevOps Engineer) to become a major part of their core-team.
Our Approach to The Project.
Our team translated business requirements into the architectural vision for the entire platform. As the most senior engineers, we also provide mentoring, conduct code reviews, and assist with establishing best practises. Additionally, we actively develop core elements of the ecosystem.
Preparing a prototype with MVP functionality (APIs, basic blockchain architecture, microservices architecture) to prove to investors and potential clients that Amini has the capability to collect and process the base data and integrate its system with the external API, as well as lay the foundation for integration with future components.
The prototype enables the user to input the farm’s geographical coordinates and returns details such as the relevant crop calendar for that land allotment and base soil parameters, enabling the user to understand and predict future events that may affect their harvest.
Translating the existing features into a business solution in the form of a microservice platform by adding authentication processes and deploying the system on AWS with the use of Kubernetes according to the best standards of security and scalability. Another important part of the phase was introducing the data queuing systems as well as preparing the frontend part of the project to provide the UX/UI for the end users.
As we supplemented the team with the missing competencies, our developers are currently working on the maintenance of existing features and the integration with the new components, working towards making the solution market-ready.
– Key decisions.
The architecture deployment issues – we chose AWS as the most reliable cloud vendor because we are experienced working with vast scaling opportunities, and Kubernetes because of its autoscaling capabilities, error resistance, and auto-healing.
We decided to go with ArgoWorkflows, – the innovative processing solution that is cost-effective, extendable, scalable, and streamlines the deployment process.
By integrating authentication procedures and deploying the system on AWS via Kubernetes in accordance with the best practises for security and scalability, the current features have been transformed into a business solution in the form of a microservice platform. Data queuing technologies were also included during this phase, and the frontend of the project was ready to provide a user-friendly interface and experience.
DAC.digital played a crucial role in helping the customer bootstrap their engineering culture, fostering an environment that promotes collaboration, innovation, and best practices. Through our guidance and expertise, we supported the customer in establishing a strong foundation for their engineering team.