Real-Time Data Pipeline Implementation for Enhanced Business Analytics

As an SME in the software development industry, we are focused on optimizing our e-commerce platform solutions by incorporating real-time data analytics. Our current batch processing system delays insights, impacting timely decision-making. We aim to implement a robust real-time data pipeline to provide instant analytics using technologies such as Apache Kafka, Spark, and Airflow. The project involves creating a data mesh architecture to ensure scalable, decentralized data management. Key responsibilities include designing the data flow, setting up event streaming with Kafka, processing data in real-time using Spark, and orchestrating tasks via Airflow. The end goal is to improve data observability and empower our platform with MLOps capabilities for predictive analytics. This project must integrate seamlessly with our existing Snowflake data warehouse and enhance our analytics with interactive dashboards powered by BigQuery or Databricks. This initiative is critical to maintaining our competitive edge by delivering superior insights to our clients.