Seamless migration reduced reporting times by 50% and enhanced real-time insights across 150TB of maritime data.
A UK-based maritime company and leading provider of maritime data, analytics, and insights, serving sectors such as shipping, insurance, finance, law, and government. The company specialises in risk assessment, regulatory compliance, and predictive analytics for global maritime activities, operating a vast network of data sources, including over 500 agents in 170 countries and 60+ analysts who process maritime intelligence daily.
The client aimed to overcome scalability, performance, and cost inefficiencies in their on-premises data infrastructure. The existing data platform struggled to accommodate increasingly complex data needs, creating a major bottleneck that hindered agility in data analytics while driving up maintenance costs and performance challenges.
A multi-phased cloud migration exercise was performed involving one-time migration of legacy big data from on-premise Hive data warehouse into AWS S3. Incremental data feeds for direct ingestion into the cloud environment from sources were enabled via automated scheduled data pipelines. A high-performance Spark-based ETL layer was implemented with AWS Glue with cataloguing for faster data retrieval. Athena-based data warehouse powered by dimensional data models enabled real-time data analysis.
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