Abhay Paroha, an engineering leader with over 15 years of experience, shares his insights on cloud migration in the oil and gas sector. He discusses building a robust cloud foundation and the importance of a canonical data model for bi-temporal data. The conversation covers the shift from Java to Scala, using Kubernetes for microservices, and the challenges of managing vast production data volumes. Abhay also emphasizes real-time monitoring, the impact of cloud technology on product definitions, and lessons learned from early deployment hurdles.
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Quick takeaways
The podcast emphasizes the importance of transitioning to cloud-based solutions for improving scalability, data integration, and advanced analytics in oil and gas operations.
The discussion highlights the challenges and key strategies for implementing microservices architecture and managing vast amounts of historical data effectively during cloud migration.
Deep dives
Building a Cloud Foundation in Oil and Gas
The podcast discusses the experience of transitioning oil and gas production operations to the cloud architecture, focusing on scalability and data management. The speaker emphasizes the shift from on-premise applications to cloud-based solutions for improved data integration and advanced capabilities. By leveraging cloud technology, they aim to handle extensive historical data effectively, facilitating machine learning workflows and real-time recommendations for oil well management. This transition involved moving from desktop applications to innovative cloud solutions that use advanced analytics for tasks like well surveillance and forecasting.
The Concept of Digital Twins and Data Handling
The discussions highlight the implementation of digital twins in monitoring equipment behaviors in real-time across oil fields. By modeling physical equipment digitally, companies can analyze incidents and operational efficiencies without the need for on-site inspections. The podcast also elaborates on the difficulties of managing vast amounts of data from various oil wells, illustrating that data volume is dictated by the number of wells and the frequency of readings. The necessity of data residency close to the source is also underscored, as it aids in minimizing ingestion latencies, which is crucial for accurate data handling.
Microservices and Cloud Agnosticism
The transition from a monolithic application to a microservices architecture is a central topic, where the speaker reflects on the evolution of their cloud applications. Initially using Google Cloud Platform's App Engine, the team migrated to Kubernetes to ensure cloud agnosticism, allowing for flexibility across different cloud providers like Azure and AWS. The speaker emphasizes the importance of scalability and proactive planning in deployment strategies, enabling efficient data ingestion and processing as different demands arise. This migration included adopting actor-based frameworks for better performance in real-time data processing.
Learning and Adapting Cloud Practices
Key insights on the learning experiences during the cloud journey reveal that the team initially faced significant challenges with remote monitoring and operational strategies. As they transitioned from desktop applications, they encountered issues that emphasized the importance of real-time monitoring and proactive operational commitments. Continuous education and adaptation to cloud practices, including incorporating service reliability principles, have been crucial in evolving their workflows. The speaker advises others in the industry to prioritize cost efficiency while navigating the complexities of cloud architecture to maintain profitability and operational effectiveness.
Abhay Paroha, an engineering leader with more than 15 years' experience in leading product dev teams, joins SE Radio's Kanchan Shringi to talk about cloud migration for oil and gas production operations. They discuss Abhay's experiences in building a cloud foundation layer that includes a canonical data model for storing bi-temporal data. They further delve into his teams' learnings from using Kubernetes for microservices, the transition from Java to Scala, and use of Akka streaming, along with tips for ensuring reliable operations.