Advanced Probabilistic Entity Deduplication in Large-Scale Organizational Database Using PySpark

Company Name: H1 Insight Inc
Duration: Oct 2023 - March 2024

Project Overview: Directed a large-scale database deduplication project, leveraging advanced probabilistic models and entity matching techniques to enhance data integrity in an organizational database with over 1 million records.
Role: Data Scientist - Entity Matching Engineer

Develop and implement a sophisticated probabilistic entity deduplication system tailored for a large-scale organizational database.
Significantly reduce duplication errors and enhance the database’s accuracy and reliability.
Streamline the integration of new data entries to maintain continuous data integrity and quality.

Implementing advanced probabilistic models, such as the Fellegi-Sunter model combined with the Expectation Maximization algorithm, in a scalable deduplication process.
Managing over 1 million records with continuous new data entries while maintaining data integrity.
Researching and applying the latest techniques to address challenges in the deduplication process.

Methodology: Led the research and development of the deduplication system using PySpark, applying technical and academic knowledge to ensure a scalable approach to entity matching.
Technologies and Tools: PySpark, Fellegi-Sunter model, Expectation Maximization algorithm.
Implementation Details: Developed and optimized PySpark scripts for large-scale data processing, addressing complex data variations and inconsistencies with advanced techniques.

Successfully reduced duplication errors by approximately 40%, enhancing the database’s accuracy and reliability.
Achieved a 30% increase in operational efficiency related to data management through streamlined data integration.
Reduced manual data review efforts by 50%, thanks to the automated and accurate identification of duplicate entries.
Played a crucial role in enhancing the organization’s data-driven decision-making process, establishing a new benchmark in large-scale data deduplication.

The application of advanced probabilistic models and entity matching techniques can significantly improve data integrity and operational efficiency in large-scale databases.
Continuous research and adaptation of the latest deduplication techniques are essential for maintaining data quality and integrity.