Postgres vs. MongoDB for real-time machine learning on wind turbine data
Luis Manuel Carril
Luis is currently product owner at Swarm64, where he developed their PostgreSQL-based analytics solution for several years but now helps to solve the client problems and incorporate them into the Swarm64 technology. Previously he worked for ten years in several universities and research centers in software engineering, cloud, parallelism and high performance computing. He has a PhD in Computer Science from the Karlsruhe Institute of Technology.
Wind turbines generate a large amount of SCADA data, approximately 500 different metrics each second. Turbit Systems captures and analyzes wind turbine data in real time, using machine learning algorithms to automatically detect the slightest technical fault, recommend corrective measures, and continuously improve turbine behavior and efficiency.
In this webinar, you’ll learn why Turbit Systems chose Postgres over MongoDB to capture and analyze wind turbine data in real time, and how they run machine learning on Postgres at scale:
- The challenges of collecting machine data from wind turbines
- Switching to Postgres from MongoDB
- Engineering a Postgres-based machine learning data stack
- Accelerating Postgres with Swarm64 DA to achieve sub-second query performance
- Visualizing and interpreting wind turbine data
- 2020 June 17 11:00 UTC
- 1 h
- Postgres Webinar Series
- Case Study
- Requires Registration:
- Yes (Registered: 156)