Real-time Data Pipeline Optimization for Enhanced User Engagement on Streaming Platform

As a scale-up company in the Streaming Platforms industry, we are dedicated to providing our users with a seamless and engaging viewing experience. To achieve this, we need to optimize our data pipeline for real-time analytics, focusing on user behavior and content recommendation. The project involves deploying an advanced event streaming architecture using Apache Kafka and Spark to handle real-time data ingestion and processing. Additionally, we aim to implement data observability practices to ensure data quality and reliability. Integration with MLOps will enable us to leverage machine learning models for personalized content delivery. The project requires expertise in setting up scalable data infrastructure using Airflow for orchestration, dbt for data transformations, and Snowflake or BigQuery for analytics storage. This initiative is critical to maintaining our competitive edge by increasing user retention and engagement.