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Microsoft Fabric Updates Blog

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  2. RK Iyer

Privacy by Design: PII Detection and Anonymization with PySpark on Microsoft Fabric

  • Headshot of article author June 12, 2025 by RK Iyer 8,503 Views

Introduction Whether you’re building analytics pipelines or conversational AI systems, the risk of exposing sensitive data is real. AI models trained on unfiltered datasets can inadvertently memorize and regurgitate PII, leading to compliance violations and reputational damage. This blog explores how to build scalable, secure, and compliant data workflows using PySpark, Microsoft Presidio, and Faker—covering … Continue reading “Privacy by Design: PII Detection and Anonymization with PySpark on Microsoft Fabric”

Playbook for metadata driven Lakehouse implementation in Microsoft Fabric

  • Headshot of article author April 2, 2025 by RK Iyer 97,488 Views

Co-Author – Gyani Sinha, Abhishek Narain Overview A well-architected lakehouse enables organizations to efficiently manage and process data for analytics, machine learning, and reporting. To achieve governance, scalability, operational excellence, and optimal performance, adopting a structured, metadata-driven approach is crucial for lakehouse implementation. Building on our previous blog, Demystifying Data Ingestion in Fabric, this post … Continue reading “Playbook for metadata driven Lakehouse implementation in Microsoft Fabric”

Demystifying data Ingestion in Fabric: fundamental components for ingesting Data into a Fabric Lakehouse using Fabric Data pipelines

  • Headshot of article author June 18, 2024 by RK Iyer 144,345 Views

Co-Author – Abhishek Narain Overview Building an effective Lakehouse starts with establishing a robust ingestion layer. Ingestion refers to the process of collecting, importing, and processing raw data from various sources into the data lake. Data ingestion is fundamental to the success of a data lake as it enables the consolidation, exploration, and processing of … Continue reading “Demystifying data Ingestion in Fabric: fundamental components for ingesting Data into a Fabric Lakehouse using Fabric Data pipelines”