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Info Fusion:

Data Enrichment of Networks

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The main goal of this project was to create a data aggregation tool that could efficiently combine multiple datasets generated by various software systems and display them in an accessible way. The tool is designed to link data based on common attributes such as MAC addresses and network names. It collects and merges data from different sources to provide a comprehensive view of the information.

Group Members:

Brandon Huynh

Anthony Tan

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Problem

Our project focused on solving the problem of handling and visualizing large amounts of scattered data. Our work consolidates information from different sources into a single, organized format. By doing so, the program simplifies complex data sets and enables better decision-making through comprehensive insights.

Methods

Results

  • The system effectively aggregated inventory data from various sources, providing a unified view for users.
  • Reduced manual data entry and data reconciliation efforts, saving time and increasing operational efficiency.
  • Learned about data cleaning techniques to ensure data quality and how to work with web APIs
  • Learned about continuous improvement and iterative development in software projects.

We developed a program using Python that collected data from various sources, such as databases, APIs, and CSV files. We used data cleaning techniques to ensure data accuracy and then aggregated the information into a centralized database. In doing so, our program was able to run over 10 times faster than the previous iteration.

  • Pandas is a versatile data manipulation and analytics library used to handle structured data effectively.

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  • Cron jobs offer a method to automate the execution of the aggregation program on a scheduled basis.

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  • The aggregated data is seamlessly pushed to the TDX ticketing system using its web API, enhancing readability and accessibility for users.

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INTERNSHIP

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