Using predictive technology, Augury is making machines more reliable by combining two key shifts in the industry: artificial intelligence and the Internet of Things. The intersection of these trends allows Augury to provide visibility into the actual condition of equipment, enabling their customers to avoid surprises, make informed reliability decisions, and increase the lifetime value of equipment. To accomplish all this, Augury has become a leading authority on the health and well being of industrial equipment.
By delivering real-time insights and historical analytics about the condition of machines on the manufacturing floor, Augury helps their customers perform the right maintenance at the right time. Beyond just alerting, Augury provides actionable suggestions on the root cause of faults, recommendations on which checks to perform, and where to best focus the maintenance that’s needed to keep machines running smoothly and avert catastrophic failures. The results are impressive — averages of 75% fewer breakdowns, 30% lower asset costs, 45% higher uptime.
Today, Augury is monitoring machines in over 6,000 facilities in North America and Europe, primarily monitoring machines in industrial and commercial facilities spanning the Consumer Packaged Goods, Food & Beverage and Pharmaceutical Manufacturing industries.
To collect machine data, Augury provides sensors that measure vibration, temperature, ultrasonic, and electromagnetic emissions. Algorithms built on traditional vibration analysis knowledge are tailored to the specific metrics associated with each type of machine. These metrics are augmented by Augury’s own knowledge and data specific to machines and their vulnerabilities.
Augury initially built their predictive services against MongoDB. Yet as the company began to grow and the dataset reached the limits of MongoDB, they realized the need for data infrastructure that could scale horizontally. The Augury team evaluated Apache Cassandra. While Cassandra initially looked promising, the team realized that there was a solution that provided the same type of features and data access without requiring a solution with Apache dependencies, such as Zookeeper. For this reason and more, they began focusing their attention on Cassandra drop-in alternative ScyllaDB.
The Augury team conducted a proof of concept (POC) to evaluate ScyllaDB’s architecture and features, with a focus on ensuring that they could model machine data properly in ScyllaDB. They also wanted to ensure support for offline analytics using Apache Spark and Beam at high volumes, with high levels of parallelism. Given Augury’s multi-data center architecture, they needed to demonstrate that they could access production data from research environments.
“The system requires less management and configuration to keep it running. ScyllaDB just runs smoothly and with minimal configuration.”
Amit Ziv-Kenet, Backend Tech Lead, Augury
“ScyllaDB’s main benefit for Augury is the ability access the data in a way that precisely matches our use cases, which we had been unable to do previously,” said Daniel Barsky, Machine Learning Algorithms Developer at Augury. “With ScyllaDB, we have yet to find a use case where we needed the data in a certain way, or we needed a certain query, or a certain format of the data, and we weren’t able to achieve that simply using ScyllaDB.”
The team was delighted to discover in that process that ScyllaDB was fast enough to support real-time user interface queries as well. “We are currently using ScyllaDB for OLTP-type access with millisecond queries for actually visualizing and providing APIs over our time-series data,” Barsky added. “At the same time, we use it for our OLAP use cases using the Spark connector with Apache Beam and Telescope.” Being able to combine the two types of processing against a single cluster has reduced the cost and complexity of their data infrastructure.
“The second largest benefit of adopting ScyllaDB is the reduction in management overhead,” said Amit Ziv-Kenet, Augury’s Backend Tech Lead. “Self-hosting can be very expensive and time-consuming. We haven’t encountered any stability issues with ScyllaDB, and the system requires less management and configuration to keep it running. ScyllaDB just runs smoothly and with minimal configuration.”
Augury has migrated diagnostics and analytics data from MongoDB to ScyllaDB. With plans to expand, Augury currently runs a three-node ScyllaDB cluster in each data center, a total of six nodes.
Ziv-Kenet summed up Augury’s experience with ScyllaDB. “It’s been a smooth ride so far. With lean DevOps resources, we tried to stay away from infrastructure that is very heavy and requires loads of ongoing maintenance. ScyllaDB is perfect for us, since we don’t have the dedicated resources to babysit our database.”