For tech start-ups, building an infrastructure that doesn’t require a large upfront investment, and that is stable but flexible enough to respond to changing needs, is crucial to successful company growth. With its previous cloud provider, Raycatch encountered performance issues that were leading to system downtimes. “Docker was poorly supported, so often disks weren’t able to disconnect and on one occasion, a disk in the data center even had to be rebooted manually,” recalls Haggai.
To improve system performance, Raycatch decided to migrate its infrastructure to GCP. “As everything was Dockerized, we could just lift and shift, and it was very quick and easy,” says Haggai. “It only took a day or two, as we had excellent support from Google Cloud Partner DoiT International. They examined our existing architecture and helped us to anticipate any obstacles and challenges, which meant the move was more or less frictionless.”
“Now, we use Compute Engine to handle our processing,” Haggai explains. “We have peaks during the night, as that’s when we run our scraping and analysis, in order to have the refreshed reports ready for our clients in the morning. We use Cloud Load Balancing and autoscaling to seamlessly handle those peaks in demand.”
“The big change for us is the degree of flexibility we now have with Google Compute Engine, it’s easy to spin up clusters of different-sized VMs, or custom machine types, depending on our needs,” says Haggai. “We have also made modifications to the way we organize our architecture like shifting to in-memory processing, for example, which helps to reduce computing costs.”
Raycatch uses artificial intelligence to perform some of its data processing tasks, in order to achieve the system efficiency required to perform the necessary analyses within the daily timeframe.
“We use Cloud Bigtable as a NoSQL database to store incoming information from the solar assets, and to help us perform sophisticated calculations,” says Haggai. “A solar asset might have 10,000 sensors for humidity, temperature, and so on, and each sensor needs to be tagged and identified. We have automated this process in order to make it faster and more accurate—I’m sure anyone working with IoT can relate to this problem.”
Raycatch had set a target to remove its previous limitations on the amount of data it was able to process, and with Cloud Bigtable, discovered that it was able to meet this target. “We used Bigtable to store the results of our Phase 1 algorithm, and also relied on it for faster data fetch in Phase 2,” Haggai says. “Our algorithms use a huge amount of data so previously we were limited by this, but Bigtable has given us an answer to the problem.”
