How Opinov8 Built a Maritime Intelligence Platform with Databricks

During a large-scale transformation project in the maritime sector, we built a robust data processing pipeline using Azure Databricks to unify inputs from diverse sources. The client, a global maritime intelligence platform supporting charterers, shipowners, and operators, needed a scalable, intelligent data infrastructure capable of handling massive volumes of operational data across their fleet.

The system relied heavily on data coming through email — an often-overlooked but still widely used integration method in industries with legacy workflows. To automate this process, we built an Azure Function that detected relevant messages, extracted file attachments, and stored them in Azure Data Lake Storage (ADLS). From that point, Databricks took over.

Spark Streaming for Real-Time Processing

Once files were stored in ADLS, a series of Databricks Spark Streaming jobs began orchestrating the transformation process. These jobs cleaned, validated, and enriched each data batch — ensuring near real-time insights and high data reliability right from the start. Given that the client processes sensor readings from over 50,000 vessels, speed and consistency weren’t optional — they were mission-critical.

This processing pipeline handled more than 7 million sensor readings daily, enabling the platform to deliver intelligence on vessel performance, fuel consumption, and greenhouse gas emissions with confidence. The Databricks environment made it easy to monitor, scale, and adapt to this level of operational complexity.

Writing to Azure Hyperscale for Fast Querying

After transformation, all enriched data was written to Azure Hyperscale — a cloud-native SQL-based storage solution that effortlessly managed volume spikes without sacrificing performance. This scalability was essential, considering the unpredictable volume of maritime data and the need to integrate third-party data on the fly.

Hasura for Simplified Data Access

To expose this data to different teams and applications, we implemented Hasura’s GraphQL engine on top of Azure Hyperscale. Hasura automatically generated GraphQL queries and mutations from the database schema, which drastically reduced the need for custom backend code.

This allowed both technical and non-technical stakeholders to build features quickly, without having to understand the complexities of the underlying database. Even as the data model evolved, Hasura ensured that access layers stayed intact — lowering maintenance effort and allowing the client to stay agile.

Clean DevOps with .whl ETL Packaging

From a development standpoint, Databricks offered an efficient and clean DevOps workflow. We packaged our ETL pipelines as .whl files, enabling reproducible deployments with strict version control and dependency management. These jobs were then deployed to dedicated job clusters, keeping production environments isolated and easy to monitor.

This modular, versioned deployment setup helped us enforce code quality while simplifying collaboration between data engineers and platform developers. The client could now onboard new data sources with minimal disruption and maintain governance across environments.

Auto-Scaling for Cost-Efficiency and Flexibility

One of the most valuable features in Databricks was automated cluster scaling. The system dynamically adjusted resources based on workload demands — ensuring optimal performance during peak data ingestion times and reducing unnecessary costs during quieter periods. For a global platform that operates across time zones and depends on continuous input, this kind of elasticity wasn’t just a nice-to-have — it was essential.

Future-Proofing the Maritime Intelligence Platform with Databricks

This Databricks-powered pipeline became the backbone of the client’s maritime intelligence platform and overall cloud-based transformation. But it didn’t happen in isolation. The entire journey began with a series of Discovery Workshops, where we worked closely with stakeholders to define business goals, architectural constraints, and system requirements.

We then used rapid prototyping to validate architectural decisions early, aligning them with long-term needs such as data integration, consistency across services, and low-latency performance. The unification of platforms onto Microsoft Azure made the environment easier to scale, secure, and monitor.

Outcomes That Matter

The result? A unified data platform that enables smarter decisions across the maritime lifecycle. This includes:

• 15% reduction in fuel consumption
  
• 30% improvement in vessel performance metrics
  
• Enhanced support for greenhouse gas compliance
  

With Databricks at the core, our client now benefits from an agile, cloud-native data architecture that can evolve with their business — turning raw operational data into actionable intelligence at a global scale.

Want to learn how Opinov8 and Databricks can help you scale your data ecosystem? Let’s talk.