How to implement row-level security in serverless SQL pools
Row-level security enables you to configure your views to return different subsets of rows to the users with difference permissions. This way, the readers will get limited results based on their access rights or roles. Row-level security can be easily implemented in serverless SQL pools using the instructions in this post.Synapse Connectivity Series Part #2 - Inbound Synapse Private Endpoints
This blog article will feature Synapse Private Endpoint. The foundation of this article was based on a previous post - Azure SQL DB Private Link / Private Endpoint - Connectivity Troubleshooting) which I will go more in depth with Synapse specific features.Synapse pipelines storage event trigger security deep dive
ADF and Synapse Pipelines offer a feature that allows pipeline execution to be triggered based on various events, such as storage blob creation or deletion. This can be used by customers to implement event-driven pipeline orchestration. In this blog post, we will address some common security questions and challenges that may arise during storage event trigger creation.How-To Deploy your Synapse Workspace Artifacts to a Managed VNET Synapse Workspace
This article will demonstrate how you can use the Synapse Workspace Deployment task in Azure DevOps to deploy your Synapse Workspace artifacts to a target Managed VNET Synapse Workspace that is configured to not allow public network access.
How Data Exfiltration Protection (DEP) impacts Azure Synapse Analytics Pipelines
Do you want to know how enabling Data Exfiltration Protection (DEP) against a Synapse Workspace will impact Synapse Pipelines? This article discusses the impacts of DEP on Synapse Pipelines and potential workarounds. If you are considering enabling DEP in your Synapse deployment - this is a must read.Synapse Spark - Encryption, Decryption and Data Masking
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 is 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.
