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Named Entity Encryption in Spark

Arshad Ali wants to secure some data being used in a Synapse Spark pool:

As a data engineer, we often get requirements to encrypt, decrypt, mask, or anonymize certain columns of data in files sitting in the data lake when preparing and transforming data with Apache Spark. The extensibility feature of Spark allows us to leverage a library which is not native to Spark. One such library isĀ Microsoft Presidio, which provides fast identification and anonymization modules for private entities in text such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data, and more. It facilitates both fully automated and semi-automated PII (Personal Identifiable Information) de-identification and anonymization flows on multiple platforms.

In this blog post, I am going to demonstrate step by step how to download and use this library to meet the above requirements with Spark pool of Azure Synapse Analytics.

Read on to see how it works.