Data Management with Azure: Implement Compliance Controls

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Data Management with Azure: Implement Compliance Controls

Coursera · Intermediate ·📊 Data Analytics & Business Intelligence ·3mo ago

Key Takeaways

Implements compliance controls for data management with Azure

Original Description

Confidential data stored within a Microsoft SQL Server or Azure SQL Database should be classified and kept safe within the database. This classification allows the SQL Server users, as well as other applications, to know the sensitivity of the data that is being stored. Classification and protection of the data stored in the database is a must – implementation of row-level security can restrict row-level access based on a user's identity, role, or execution context and with the implementation of Dynamic Data Masking you can limit sensitive data exposure to non-privileged users. Using the Azure portal, you can identify, classify, and protect your sensitive data. In this intermediate-level guided project "Data Management with Azure: Implement Compliance Controls”, you will create an Azure SQL Server and set up sample database. Using sample database, sensitive data will be classified and “protected” using row level security and dynamic data masking. You will also learn what is and how to use Microsoft Defender for SQL. The requirement for this project is having a free and active Azure account and an active Azure subscription. You will be given short instructions on how to get them in the first task.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Pydantic for Data Engineering: Schema Validation in ETL & Pipeline Contracts
Use Pydantic for schema validation in ETL pipelines to ensure data consistency and quality
Dev.to · Gowtham Potureddi
📰
Half of Data Engineering Jobs on LinkedIn Aren't Real
Understand the discrepancy between reported data engineering job growth and actual job availability on LinkedIn
Dev.to · DataDriven
📰
Evolutionary Data Through Schemaboi: Achieving Forward, Backwards, and Sideways Compatibility
Learn how Schemaboi achieves forward, backwards, and sideways compatibility for evolutionary data through self-contained schemas in file headers
InfoQ AI/ML
📰
How Morphohack Helped Me Recover €678,000 in Lost Crypto Assets
Learn how Morphohack helped recover €678,000 in lost crypto assets using data science techniques
Medium · Data Science
Up next
6-Phase SQL Roadmap 2026 | Data Analytics & Engineering | #shorts
SCALER
Watch →