How Windward Built Real-Time Logistics Tracking and AI Insights for the Maritime Industry
Customer Company Size
Large Corporate
Country
- United States
Product
- Rockset
- Snowflake
Tech Stack
- API First Approach
- Converged Indexing
- SQL on Semi-Structured Data
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Marine & Shipping
Applicable Functions
- Logistics & Transportation
- Business Operation
Use Cases
- Fleet Management
- Predictive Maintenance
- Remote Asset Management
Services
- Data Science Services
- System Integration
About The Customer
Windward is a leading Maritime AI company that provides an all-in-one platform for risk management and maritime domain awareness to accelerate global trade. The company monitors and analyzes over 500,000 vessels worldwide, providing actionable intelligence with real-time ETA tracking, carrier performance insights, risk monitoring, and more. With 90% of trade being transported via sea, Windward's data is crucial for keeping the global supply chain on track. The company serves a diverse range of customers, including suppliers, carriers, governments, and insurance companies, who use maritime data for various purposes such as determining price, assessing risk, and monitoring illegal activities. Windward's platform is designed to support the rapidly shifting needs of the maritime ecosystem, giving its customers the visibility and AI insights needed to respond and stay compliant.
The Challenge
Windward faced challenges with its existing data stack, which was not ideal for the search and analytical features needed to transform the maritime industry. The company was using a batch-based data stack with multiple databases, including S3, MongoDB, PostgreSQL, Cassandra, and Elasticsearch, which made it difficult to support new use cases and achieve performant contextual queries. The existing data stack also limited the ability to generate significant new revenue from product requests that were hard to support, such as geo queries, vessel search, and partial word search. Additionally, the AIS transmission data was flaky, making it challenging to associate a transmission with the right vessel, and the enrichment data had changing schemas, making it difficult to support using relational databases with strict schemas.
The Solution
Windward decided to embark on a new data stack that could better support the logistics tracking needs of their customers and the maritime industry. They evaluated more than 10 different databases and shortlisted Rockset and Snowflake for their ability to support the main use cases for search and analytics. Rockset was chosen for its fast search and analytics on streaming data, API first approach, and support for in-place updates. It also allowed Windward to consolidate geo data and time series data into one system and query using SQL. Snowflake was evaluated for its columnar store and ability to support large-scale aggregations and joins on historical data. Both Rockset and Snowflake are cloud-native and fully-managed, minimizing infrastructure operations on the Windward engineering team. The performance evaluation showed that Rockset achieved faster query performance at less than half the price of Snowflake, making it the preferred choice for Windward's use case.
Operational Impact
Quantitative Benefit
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