Scalable Real-time Data Pipeline for Enhanced Customer Insights

As a rapidly growing startup in the Software Development industry, we face the challenge of integrating and analyzing vast amounts of user data to improve our product offerings. We aim to build a real-time data pipeline that can seamlessly ingest, process, and analyze data from multiple sources to provide actionable insights. The project involves setting up a robust data infrastructure using Apache Kafka for event streaming, Spark for real-time analytics, and orchestrating workflows with Airflow. We also plan to implement a data mesh architecture to facilitate decentralized data ownership and enhance collaboration across teams. The data pipeline will integrate with Snowflake for data warehousing and BigQuery for advanced analytics, ensuring scalability and performance. By adopting MLOps practices, we aim to streamline the deployment and monitoring of machine learning models to deliver personalized customer experiences.