Real-Time Mental Health Data Platform Enhancement

We are a growing SME in the Mental Health & Wellness industry, seeking to enrich our existing data processing ecosystem. The objective is to integrate real-time analytics into our data platform to enhance our understanding of mental health trends, patient outcomes, and service efficiency. We aim to implement a robust data architecture that supports event streaming and real-time data processing. The project involves utilizing Apache Kafka for event streaming, Apache Spark for real-time data processing, and orchestrating workflows with Apache Airflow. The final data storage and analytics layer will leverage platforms like Snowflake or BigQuery for scalable data warehousing and analysis. Furthermore, we hope to apply dbt for data transformations and ensure data observability throughout the pipeline. By doing so, we can deliver timely insights to therapists, counselors, and other stakeholders, enhancing decision-making processes. This project will require close collaboration with our data science team to ensure that machine learning operations (MLOps) are seamlessly integrated, thereby improving our predictive models' deployment and monitoring. We anticipate the project duration to be 12-16 weeks, with a budget allocation of $25,000 - $75,000, and urgency marked as medium.