In today’s digital age, managing data from various sources can be challenging for organizations, leading to issues in data integration and governance. AWS Glue is a serverless data integration service that streamlines the process of discovering, preparing, moving, and integrating data for analytics, machine learning, and application development.
Entity resolution is a crucial aspect of data governance, involving linking data from different sources that represent the same entity, even if they are not identical. This process is essential for maintaining data integrity and avoiding duplication that could impact analysis and insights.
Built on the Apache Spark framework, AWS Glue offers the flexibility to enhance its capabilities through third-party Spark libraries like Zingg, an ML-based tool specifically designed for entity resolution in Spark.
To utilize Zingg’s entity resolution capabilities within an AWS Glue notebook and run it as an extract, transform, and load (ETL) job, organizations can effectively address data governance challenges and ensure consistent and accurate data across their operations.
The solution outlined involves using Zingg to deduplicate a dataset of posts with slightly different attributes by leveraging a third-party entity resolver. The process includes configuring the Zingg library and related files, preparing training data, building a model, and finding matches using the ML-based tool.
By incorporating third-party Apache Spark libraries like Zingg, organizations can extend the capabilities of AWS Glue and have the flexibility to use their own data effectively. This entity resolution process can be integrated into workflows using tools like Amazon Managed Workflows for Apache Airflow (Amazon MWAA).
Overall, the integration of third-party tools like Zingg with AWS Glue offers organizations the freedom of choice and customization to enhance data governance efforts and ensure the accuracy and consistency of their data. This collaborative approach enables organizations to derive valuable insights from their data and streamline their operations for better decision-making.
Article Source
https://aws.amazon.com/blogs/big-data/entity-resolution-and-fuzzy-matches-in-aws-glue-using-the-zingg-open-source-library/
