Crystal-clear visibility into cloud spend at the workload and tenant level
- Days
- Time to first cost insights after install
- Days
- From install to powerful cost reporting
- Workload & tenant
- Granularity of cost visibility
Meet Raycatch
As the green energy sector continues to expand, Tel Aviv-based Raycatch developed the DeepSolar™ software to provide solar asset owners with data insights to optimize their photovoltaic plants. Founded in 2015, the company works with major European utility companies including Arava Power, RTR, EDF, and Enlight, and is expanding into China, India, and other markets. Raycatch analyzes existing data streams from solar assets without installing new hardware, using sophisticated algorithms to provide clients with concrete daily action items.
The Challenge
Raycatch faced performance issues with their previous cloud provider including system downtimes, poor Docker support, and disk connectivity problems. Their complex solar asset data analysis system needed to process massive amounts of data within 24-hour cycles, but infrastructure limitations were constraining their ability to scale and serve more clients.
The Solution
Raycatch migrated to Google Cloud using a lift-and-shift approach with their Dockerized infrastructure. They implemented Google Compute Engine for processing with autoscaling and load balancing to handle peak demands during nightly analysis runs. Cloud Bigtable serves as their NoSQL database for storing and processing solar asset sensor data from thousands of IoT devices.
Results
- Reduced analysis time from 3 days to under 5 hours
- Cut infrastructure costs by at least 80% compared to previous provider
- Decreased work hours for simple analysis by 60 times
- Improved system stability and flexibility with better Docker support
- Enabled processing of unlimited data volumes with Cloud Bigtable
It only took a day or two, as we had excellent support from DoiT. They examined our existing architecture and helped us to anticipate any obstacles and challenges, which meant the move was more or less frictionless.
Haggai Hofland, CEO, Raycatch
AI-Powered Data Processing
Raycatch uses artificial intelligence to achieve the system efficiency required for daily analysis timeframes. Solar assets can have 10,000 sensors measuring humidity, temperature, and other variables. Each sensor must be tagged and identified, a process Raycatch has automated for speed and accuracy. Cloud Bigtable stores Phase 1 algorithm results and enables faster data fetch for Phase 2 processing, removing previous limitations on data processing volumes.
What's Next
Raycatch plans to continue expanding AI automation and growing their client base. With Google Cloud eliminating infrastructure bottlenecks, their AI can run on all data with parallelized analysis efforts. This enables them to take on more clients and projects while remaining agile and adapting smartly as they grow. The improved infrastructure has positively impacted their service and product development capabilities.
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