Real-time Data Pipeline Implementation for Shipping Efficiency

As a burgeoning startup in the shipping & freight industry, we face the challenge of optimizing our operational efficiency amidst dynamic market conditions. We need a robust real-time data pipeline that allows us to process and analyze vast amounts of data generated from various points in our logistics network. The goal is to provide real-time insights that facilitate swift decision-making, enhance cargo handling, reduce delays, and improve overall customer satisfaction. The project involves utilizing technologies such as Apache Kafka for event streaming, Spark for fast data processing, and Airflow for workflow automation. We aim to integrate these with our existing data warehousing solutions like Snowflake or BigQuery. This infrastructure will form the backbone of our data-driven decision-making process, enabling us to respond swiftly to changes in demand and supply, optimize routes, and predict maintenance needs. The implementation of a data mesh architecture will ensure scalable and decentralized data management while MLOps practices will improve our model deployment capabilities. We seek a freelancer with a proven track record in data engineering, particularly in the shipping & freight sector, who can deliver a solution within 4-6 weeks. The project is urgent given our operational goals and market pressures.