Ceto is revolutionizing maritime operations with MongoDB Time Series

INTRODUCTION

Pioneering predictive analytics in maritime logistics

The maritime industry has long relied on manual processes, with crews maintaining extensive paper logs to track vessel performance and maintenance needs. Now outdated, the maritime industry’s reliance on paper records often results in inefficiencies, delayed responses, and a reactive rather than proactive approach to maintenance. These shortcomings can lead to unexpected machinery breakdowns, inefficient fuel consumption, and increased operational risks.

Ceto is on a mission to change this. Using the power of artificial intelligence, the company is working to bring the maritime industry into the digital age—and to transform maritime operations into a model of efficiency and sustainability. Founded in 2020 by Tony Hildrew, a seasoned maritime logistics expert, and Ben Harrison, an experienced software developer, Ceto is redefining industry standards by utilizing advanced predictive analytics. Its mission is to prevent machinery breakdowns, reduce fuel consumption, lower carbon emissions and deliver the first connected marine insurance policy.

To make its mission a reality, Ceto partnered with MongoDB, leveraging its robust data handling capabilities to integrate AI with real-time data collected from thousands of sensors across its customers’ fleets. This allows Ceto to predict and preempt potential failures, streamline operations, and manage risks proactively. This shift not only enhances safety and reliability but also propels maritime logistics into a new era of technological advancement, making Ceto a transformative force in global commerce.

THE CHALLENGE

Scaling data management in maritime logistics

Initially, Ceto relied on InfluxDB to manage the massive amounts of data streaming from sensors installed across their fleet. However, they quickly confronted serious scalability issues and reliability concerns. In the high-stakes maritime industry, where delays translate directly into significant financial losses and elevated environmental impacts, these challenges were unacceptable.

“The existing data management system simply wasn’t capable of scaling with our rapid growth,” said Ben Harrison, CTO at Ceto. “We urgently needed a robust platform that could handle our high-frequency data seamlessly and without interruptions.”

Ceto’s previous system struggled to manage the sheer volume and velocity of data generated by the fleet. Each vessel is equipped with thousands of sensors, each operating at a frequency of 50–100Hz, which translates to 300 to 400MB of raw data per vessel daily. This results in approximately 90,000 JSON documents per day, each storing around 100 unique time-series measurements. The existing system was overwhelmed by this data influx, leading to inefficiencies and the risk of downtime. Any system downtime or inefficiency could severely impact their ability to make timely, data-driven decisions, negatively affecting customer service and jeopardizing safety protocols.

In their quest for a more capable system, Ceto evaluated several alternatives. The industry’s historical reliance on manual processes and logbooks meant that transitioning to a digital, AI-driven approach would be a significant shift. “We were turning away from the industry norm of manual logs and intermittent data reviews,” said Ceto’s CEO, Tony Hildrew. “Our aim was to implement a system that could leverage real-time data for immediate and actionable insights, a radical change that would set new operational standards in maritime logistics.”

Their search led them to consider MongoDB Atlas, in particular MongoDB Time Series Collections. Specifically, Ceto was looking for a database solution that wouldn’t unexpectedly drop support or functionality. The company needed a solution that promised scalability, ease of management, and reliability to support their innovative approach to maritime operations.

THE SOLUTION

Adopting MongoDB’s Time Series Collections

Faced with the challenge of managing vast volumes of high-frequency data from its sensors, Ceto chose Time Series Collections for its robust scalability, ease of management, and strong reliability.

Ceto’s migration to Time Series Collections involved setting up a cluster tailored to handle the intensive demands of the company’s data throughput. This test setup was accomplished in just a few months, a rapid deployment that minimized disruption to ongoing operations. The new production system was eventually configured to process thousands of data points per second per vessel, ensuring that real-time data feeds were captured and analyzed efficiently.

MongoDB’s architecture provided Ceto with several key features that were crucial for their operations. The scalable clusters facilitated by MongoDB Atlas allowed Ceto to grow with their data needs without requiring a significant amount of downtime or reconfiguration. This scalability was essential for managing the increasing data volumes generated by their expanding fleet. Additionally, Time Series Collections offered advanced data compression capabilities, crucial for managing the large volumes of data generated daily. This optimization ensured that storage was efficient and cost-effective.

Moreover, MongoDB enabled real-time data processing, allowing Ceto to achieve immediate analysis and actionable insights. This real-time capability was vital for the predictive analytics that underpinned Ceto’s mission to optimize vessel operations and reduce downtime.

This technology shift not only aligned with Ceto’s need for a more robust data handling solution but also equipped them with a platform that could evolve with their expanding operations. By adopting MongoDB's Time Series Collections, Ceto set the stage for continuous innovation and operational excellence in the maritime industry.

THE RESULTS

Enhancing efficiency and reducing environmental impact

The implementation of MongoDB’s Time Series Collections marked a significant transformation in Ceto’s operational capabilities. One of the most notable outcomes was the dramatic improvement in data compression—reducing data storage needs from 300MB to 3MB per vessel per day. This efficiency not only reduced costs associated with data storage, but also streamlined the process of data analysis and retrieval.

The enhanced data handling and compression capabilities led to a decrease in the cost of data storage and processing. Predictive maintenance capabilities extended machinery lifespan and reduced downtime, saving approximately $20,500 per vessel annually. These savings were critical in managing operational costs and improving overall efficiency.

Additionally, MongoDB’s real-time data processing and analytics enabled Ceto to enhance its service level agreements (SLAs) by providing faster and more accurate predictive insights. This improvement in data analysis allowed Ceto to offer better service to their customers, increasing satisfaction and operational reliability.

These advancements have positioned Ceto at the forefront of the maritime industry’s technological evolution. By optimizing data storage, reducing operational costs, and enhancing predictive analytics, Ceto has not only met their immediate needs but also paved the way for future growth and innovation. The successful implementation of MongoDB’s Time Series Collections has enabled Ceto to set a new standard in maritime logistics, showcasing how advanced technology can drive efficiency and sustainability in even the most challenging environments.

Looking ahead, Ceto plans to scale their operations significantly. By the end of Q2 2024, they aim to support up to 20 vessels, managing approximately 1.8 million documents and 8GB of raw data daily. In the next two to three years, this scale is expected to grow to 1,000 vessels, handling around 180 million documents and 800GB of raw data per day. With these improvements, Ceto continues to lead the charge towards a more efficient, reliable, and environmentally friendly maritime industry, demonstrating the power of innovation and scalability in driving industry-wide transformation.

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