Theis: Adaptiveness in data handling for efficient data pipelines
Data ingestion is a critical aspect of any data project. For a company to be data driven, it is critical for data to be available to the analysts and engineers in the fastest way possible, with data veracity intact.
This thesis project focuses on creating a multi-tenant streaming framework across multiple tiers in the edge-cloud/data-center continuum, to keep pace with increasing volumes and rates of data generated in digitalized industrial processes.
The project will study existing industrial multi-tier data pipelines to support pre-processing and prioritization for enhancing the efficiency, scalability and robustness. The challenges include creating a multi-tenant streaming framework, with parallelization and prioritization of the processing tasks.
Suitable background
Distributed Computing & System
Description of thesis work
The aim of this project is to create a multi-tenant streaming framework for faster data processing. It will study existing industrial multi-tier data pipelines to support pre-processing and prioritization for enhancing the efficiency, scalability and robustness.
Intended approach:
Study of relevant theories and principles related to multi-tenant streaming framework
Design the streaming framework
Parallelization and prioritization of data processing by learning the data properties
Thesis Level: Master
Language: Thesis to be written in English
Starting date: 2024-01-15
Number of students: 2 (Two)
Tutor
Binay Mishra, binay.mishra@volvo.com, Digital Product Manager - Build and Test Data
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