Aventra Group

The maritime client relied on legacy data ingestion processes that were inconsistent and prone to failures. There was no standardized approach for logging or alerting, making it difficult to monitor pipeline execution and identify failures across data ingestion workflows.

Aventra Group implemented a reusable, parameter-driven ingestion framework in Azure Data Factory (ADF) to support SQL, API, SFTP, and file-based data sources. Custom logging pipelines were developed to capture pipeline status, data volumes, execution duration, and errors in Azure Synapse tables. The solution also leveraged Synapse pipelines and Spark notebooks for data transformation, cleansing, and curation using a Medallion Architecture. Azure DevOps was used to automate the deployment of ADF pipelines and Synapse artifacts across environments.

The client lacked a real-time monitoring system to detect early-stage corrosion in pipelines. This resulted in delayed detection, increased safety risks, and higher maintenance costs.

Aventra Group built a continuous data ingestion pipeline in Azure Data Factory (ADF) to collect IoT sensor data, including corrosion rates, temperature, pressure, and humidity, from field devices and supervisory systems. Azure Synapse Spark was used for real-time anomaly detection, trend analysis, and corrosion pattern modeling. The solution included schema validation, missing-data checks, drift detection, and outlier filtering using Synapse Data Flows and Spark. CI/CD pipelines were implemented for ADF and Synapse artifacts to maintain consistent deployment across development, testing, and production environments.