Problem Statement
The company faced bottlenecks in its data processing workflows, which affected overall operational efficiency. There was also a need to assess weaknesses in resource allocation and optimize processes to improve scalability and performance across its farming operations.
Approach & Solution
To resolve these inefficiencies, we designed a solution utilizing advanced graph database technologies and scalable cloud infrastructure.
- We implemented Neo4J for graph database optimization, handling complex data relationships and enabling more efficient data querying and processing.
- Integrated AWS Neptune to ensure scalability, supporting over 500,000 records for resource allocation and farming analytics.
- Utilized AWS Lambdas with Node.js to automate key data-handling tasks, reducing manual intervention and enhancing system performance.
- Optimized data processing workflows, allowing the system to handle 25% more data in real time without performance degradation.
Results & Outcomes
The improved data processing system allowed for faster handling of complex farming data, and by optimizing resource allocation workflows, the company reduced downtime and improved system efficiency. The automation of manual tasks through AWS Lambdas saved the team significant time and increased overall productivity.
Tools & Technologies used
- AWS
- Neptune
- Neo4j
- Node.js
- AWS Lambdas
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