Dr Sreepathy H V
Assistant Professor- Selection Grade
沙巴体育 School of Information Sciences
CURRENT ACADEMIC ROLE & RESPONSIBILITIES
- Sreepathy H V is an Assistant Professor – Selection Grade at the 沙巴体育 School of Information Sciences (MSIS), 沙巴体育 Academy of Higher Education (MAHE).
- He serves as the Program Coordinator for the ME Cloud Computing program and teaches core subjects in Cloud Computing, and Big Data Analytics programs.
- He combines academic instruction with applied cloud architecture, DevOps pipeline automation, and data engineering projects aligned with modern industry practices.
- Cloud Computing Architectures and Virtualization
- DevOps Pipeline Automation and Continuous Delivery
- Experienced in designing, deploying, and managing cloud-native systems on Kubernetes, with specialization in data engineering, observability, and progressive delivery automation.
- Architect and manage data pipelines using Spark, Kafka, and Airflow deployed on Kubernetes clusters for high-throughput batch and streaming data processing.
- Implement end-to-end observability stacks with Prometheus, Grafana, Loki, and Jaeger, enabling real-time monitoring, tracing, and logging across microservices. Designed custom dashboards and alerting rules for proactive diagnosis and reliability tracking.
- Applied progressive delivery techniques such as Blue-Green, Canary, and A/B deployments using Istio service mesh and GitOps tools (ArgoCD, FluxCD). Integrated CI/CD pipelines for automated version rollouts, rollback safety, and controlled feature exposure.
- Built automated self-healing observability pipelines using multi-agent AI workflows (RAG-based LLM agents), enhancing Kubernetes cluster resilience and operational intelligence.
- Embedded System Design and Computer Architecture
- Cloud Platforms: AWS, GCP, OpenStack
- DevOps Toolchain: Jenkins, Docker, Kubernetes, Ansible, Terraform, GitOps (ArgoCD / FluxCD)
- Big Data & Data Engineering: Spark, Hadoop, Lambda Architectures, Kafka, Airflow, MinIO.
- Observability: Prometheus, Grafana, ELK, Loki, Jaeger
- Embedded Systems & Microcontrollers: 8051, ARM Cortex, FPGA, RTOS Integration
- Data Lake Governance & Metadata Management
- Cloud Automation & Infrastructure as Code
- Scalable Multi-Cloud Architectures
- AI and LLM integration for Cloud Operations
- 沙巴体育 Methodology FDP – GMIT, Davangere
- Conducted a specialized Cloud Foundations course for Schneider Electric engineers under ???MAHE’s Work Integrated M.Tech program, focusing on applied AWS cloud services.
- AWS Cloud Foundations and Architecting – GMIT, Davangere. (15 Days – Online)
- Cloud Computing for IoT Applications – JNNCE, Shivamogga
- FPGA & ARM Embedded Systems Workshops – 沙巴体育
- Agentic AI Troubleshooting – Kubernetes RAG Pipeline
AI-based self-healing observability system integrating Prometheus, Postgres, and MinIO. https://github.com/sreepathysois/Agentic-AI-Troubleshooting-Kubernetes-RAG-Pipeline - End-to-End Data Engineering Pipeline
Batch and streaming ETL architecture using Spark, Kafka, and Airflow. https://github.com/sreepathysois/End-to-End-Data-Engineering-pipeline - AWS Café Cloud Architecture
Designed multi-region AWS setup for static and dynamic applications with HA and DR.? https://github.com/sreepathysois/Cafe_Dynamic_Website - Microservices and Data Observability Stack
Built observability framework with Prometheus, Grafana, Loki, and Jaeger. https://github.com/sreepathysois/Cafe_Microservices_Data-Observability_Service_mesh-Stack - Ansible Automation for Deployment
Automated app and infra provisioning across Linux hosts.? https://github.com/sreepathysois/AnsibleWorkUpdated
Academic and Technical Competencies
Technical Expertise
沙巴体育 Focus
Workshops Conducted
Academic & Applied Projects (with GitHub Links)
Certifications
AWS Academy Graduate – Cloud Foundations, Cloud Architecting, Cloud Operations, Machine Learning (2021)
SUBJECTS CURRENTLY TEACHING
| Subject | Semester / Year |
|---|---|
| Cloud Foundations and Architecting | I Semester - ME (Cloud Computing) |
| DevOps for Cloud | II Semester - ME (Big Data Analytics) |
| Cloud Operations | II Semester - ME (Cloud Computing) |
| Cloud Architecture and Management | I Semester - ME (Cloud Computing) |
ACADEMIC QUALIFICATIONS
| Degree | Specialisation | Institute | Year of passing |
|---|---|---|---|
| B.E. | Electronics & Communication | Vidyavardhaka College of Engineering, VTU | 2009 |
| MS | Embedded System | 沙巴体育 School of Information Sciences, 沙巴体育 Academy of Higher Education | 2011 |
| Ph.D. | Data Lake Governance and Metadata Management | 沙巴体育 Academy of Higher Education (MAHE) | 2025 |
Experience
| Institution / Organisation | Designation | Role | Tenure |
|---|---|---|---|
| 沙巴体育 School of Information Sciences (MSIS) | Guest Faculty | January 2012 to May 2012 | |
| 沙巴体育 School of Information Sciences (MSIS), MAHE | Assistant Professor – Selection Grade | Teaching & Mentoring, Program Coordination (Cloud Computing), 沙巴体育 in Cloud and DevOps Architectures | May 2012 – Present |
Title: Design of an Efficient Metadata Management System and Governed Access Zone for Data Lake
The rapid growth of big data has made data lakes vital for managing heterogeneous datasets, yet traditional models face challenges in metadata management, governance, and discovery. This research introduces an integrated framework combining a Metadata Management System as a Service (MMaaS) and a Governed Access Zone (GAZ) for secure, compliant, and scalable data management.
Key Outcomes:
- Built on MinIO object storage for domain-based organization
- Achieved 75% faster metadata retrieval and 30% higher resource efficiency.
- Introduced a self-service ingestion framework for structured and semi-structured data.
- Implemented policy-driven data sharing aligned with GDPR, HIPAA, ISO 27701, NIST, ISO 27019 standards.
- Validated using an agriculture analytics use case for Karnataka, demonstrating governed, metadata-driven insights for real-time applications.
Publications:
- Ph.D. Thesis: Design of an Efficient Metadata Management System and Governed Access Zone for Data Lake (2025)
- Sreepathy H V, B Dinesh Rao, Mohan Kumar J, Deepak Rao B, “Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data lake”, published in MethodsX Journal. Indexed in SCOPUS.
- Sreepathy H V, B. Dinesh Rao, M. K. Jaysubramanian and B. Deepak Rao, "Data Ingestions as a Service(DIaaS): A Unified interface for Heterogeneous Data Ingestion, Transformation, and Metadata Management for Data Lake," in IEEE Access.
- Remote Access of Medical Image Processing and 3D Visualization Application using Raspberry Pi', 沙巴体育 Journal of Engineering & Technology.
沙巴体育 Conferences:
- Sreepathy H V, B Dinesh Rao, Mohan Kumar J, Deepak Rao B, “Architectural Inclusion: A Governed Access Zone for Secure and Compliant Sharing of Critical Data with End-Users in a Data Lake”, presented in Computational Methods in Engineering 沙巴体育 & Health Sciences (ICCMEH 2023). (Presented in Conference).
- Sreepathy H V, B Dinesh Rao, Mohan Kumar J, Deepak Rao B, “Data Discovery as a Service for Data Lake” presented and in 2024 Second 沙巴体育 Conference on Networks, Multimedia and Information Technology (NMITCON). (Presented in Conference). (Published) in IEEE Xplore (https://ieeexplore.ieee.org/document/10699255).
AREAS OF INTEREST, EXPERTISE AND RESEARCH
Area of Interest
Cloud Computing Architectures and Virtualization, DevOps, Infrastructure as Code (IaC), and Continuous Delivery Systems, Data Engineering and Data Lake Management, AI-Driven Observability and Self-Healing Cloud Systems, Kubernetes-based Microservices Deployment and Progressive Delivery, Big Data Analytics and Real-Time Data Pipelines, Embedded Systems Design and Computer Architecture, Internet of Things (IoT) and Edge Computing
Area of Expertise
Cloud Platforms: AWS, GCP, OpenStack, DevOps & Automation Tools: Jenkins, Docker, Kubernetes, Ansible, Terraform, CircleCI, GitOps & Progressive Delivery: ArgoCD, FluxCD, Istio, Blue-Green & Canary Deployments, Data Engineering & Analytics: Apache Spark, Kafka, Airflow, MinIO, PostgreSQL, Data Observability: Prometheus, Grafana, Loki, ELK, Jaeger, Programming & Scripting: Python, Shell, SQL, YAML. Embedded Systems: ARM Cortex, FPGA, 8051, RTOS Integration.