AI SearchFloware BP-floware data batchprocessing : setting up cloud data batch processing infrastructure using microsoft Azure batch
Call Number: AIT ISPR DSAI no.25-01 Material type:
TextSeries: Asian Institute of Technology. Internship Report ; no. DSAI-25-01Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2025Description: 47 leaves : ill.+ 1 online resourceSubject(s): Online resources: Dissertation note: Internship Report (M. Eng.) - Asian Institute of Technology, 2025 Summary: This 6-month internship report highlights the successful implementation of an automated cloud data engineering pipeline at Floware, a startup specializing in mobility flow analysis. The primary objective focused on designing and deploying an Azure Batch Processing solution to streamline the company{u2019}s data processing workflows.Commencing in September 2024 and concluding in March 2025, the internship centered on transforming manual data processing tasks into an automated, scalable cloud solution.The implementation involved creating containerized environments using Docker, establish ing efficient data flows between Azure services (Batch, Container Registry, Blob Storage), and developing standardized processing workflows for both computer vision and Bluetooth sensor data. Beyond the primary cloud engineering focus, serving as the company{u2019}s sole data scientist necessitated fulfilling various critical responsibilities, including data engineering, analysis, and visualization for client needs. Complex data transformation tasks were successfully automated, from processing raw sensor data to generating actionable insights such as Origin-Destination matrices and other related trajectory,Speed and Frequentation analyses from Bluetooth and Computer Vision Data . The outcomes demonstrated significant improvements in processing efficiency, with automated batch processing reducing manual intervention while ensuring consistent, reliable and efficient results. Additional achievements included developing visualization solutions using PowerBI, contributing to physical sensor deployment operations, and providing data-driven solutions for specific client requirements. This internship fostered valuable experiences in cloud engineering, data science, and startup operations, contributing to a deeper understanding of implementing scalable data solutions in real-world applications.
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Asian Institute of Technology Library Archives | AIT ISPR DSAI no.25-01 (Browse shelf(Opens below)) | 1 | Not for loan |
An internship report submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Data Science and Artificial Intelligence
Internship Report (M. Eng.) - Asian Institute of Technology, 2025
This 6-month internship report highlights the successful implementation of an automated cloud data engineering pipeline at Floware, a startup specializing in mobility flow analysis. The primary objective focused on designing and deploying an Azure Batch Processing solution to streamline the company{u2019}s data processing workflows.Commencing in September 2024 and concluding in March 2025, the internship centered on transforming manual data processing tasks into an automated, scalable cloud solution.The implementation involved creating containerized environments using Docker, establish ing efficient data flows between Azure services (Batch, Container Registry, Blob Storage), and developing standardized processing workflows for both computer vision and Bluetooth sensor data. Beyond the primary cloud engineering focus, serving as the company{u2019}s sole data scientist necessitated fulfilling various critical responsibilities, including data engineering, analysis, and visualization for client needs. Complex data transformation tasks were successfully automated, from processing raw sensor data to generating actionable insights such as Origin-Destination matrices and other related trajectory,Speed and Frequentation analyses from Bluetooth and Computer Vision Data . The outcomes demonstrated significant improvements in processing efficiency, with automated batch processing reducing manual intervention while ensuring consistent, reliable and efficient results. Additional achievements included developing visualization solutions using PowerBI, contributing to physical sensor deployment operations, and providing data-driven solutions for specific client requirements. This internship fostered valuable experiences in cloud engineering, data science, and startup operations, contributing to a deeper understanding of implementing scalable data solutions in real-world applications.
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