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    <title>Data DevOps Lab</title>
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    <description>Technical writing on Microsoft Fabric, Azure, Spark, data engineering, and DevOps.</description>
    <language>en-us</language>
    <lastBuildDate>Thu, 30 Apr 2026 20:13:40 GMT</lastBuildDate>
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      <title><![CDATA[VACUUM vs CDF in Microsoft Fabric: How to Protect Incremental Loads from Silent Data Loss]]></title>
      <link>https://datadevopslab.pages.dev/blog/vacuum-vs-cdf-how-to-protect-incremental-loads-in-microsoft-fabric</link>
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      <pubDate>Wed, 08 Apr 2026 00:00:00 GMT</pubDate>
      <description><![CDATA[VACUUM keeps Delta tables healthy, but it can also break Change Data Feed replay windows. This walkthrough shows exactly when incremental pipelines fail and how to design a safe Fabric pattern with guardrails and fallback recovery.]]></description>
    </item>
<item>
      <title><![CDATA[Implementing Change Data Feed for Incremental Processing in Microsoft Fabric Lakehouses]]></title>
      <link>https://datadevopslab.pages.dev/blog/implementing-change-data-feed-for-incremental-processing-in-microsoft-fabric-lakehouses</link>
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      <pubDate>Fri, 27 Mar 2026 00:00:00 GMT</pubDate>
      <description><![CDATA[Most lakehouse pipelines are still doing one expensive thing repeatedly: reprocessing entire tables, even when only a tiny fraction of rows has changed. This post explains the CDF-driven load pattern, why it works, where it can hurt, and how to implement it safely in Microsoft Fabric.]]></description>
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<item>
      <title><![CDATA[How Microsoft Fabric Mirroring Works for Azure SQL Database — A Technical Deep Dive]]></title>
      <link>https://datadevopslab.pages.dev/blog/how-microsoft-fabric-mirroring-works-for-azure-sql-database-a-technical-deep-dive</link>
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      <pubDate>Tue, 17 Mar 2026 00:00:00 GMT</pubDate>
      <description><![CDATA[Introduction When building a modern lakehouse on Microsoft Fabric, one of the most powerful — and often underexplored — features is Mirrored Databases. Instead of...]]></description>
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<item>
      <title><![CDATA[Data Quality Made Simple: A Quick Guide to Microsoft Fabric + Purview Setup]]></title>
      <link>https://datadevopslab.pages.dev/blog/data-quality-made-simple-a-quick-guide-to-microsoft-fabric-purview-setup</link>
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      <pubDate>Thu, 13 Nov 2025 00:00:00 GMT</pubDate>
      <description><![CDATA[Data quality isn't just a checkbox — it's the backbone of reliable analytics. Learn how to set up automated quality checks and profiling with Microsoft Purview and Fabric Lakehouse.]]></description>
    </item>
<item>
      <title><![CDATA[How to Use Concurrent Sessions in Fabric Data Pipelines]]></title>
      <link>https://datadevopslab.pages.dev/blog/how-to-use-concurrent-sessions-in-fabric-data-pipelines</link>
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      <pubDate>Thu, 28 Aug 2025 00:00:00 GMT</pubDate>
      <description><![CDATA[Speed up your Fabric pipelines by running notebooks in parallel — without breaking your Spark cluster. Learn how High Concurrency Mode works and how to configure session tags.]]></description>
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<item>
      <title><![CDATA[Run Spark SQL in Fabric Notebooks Without Attaching a Default Lakehouse]]></title>
      <link>https://datadevopslab.pages.dev/blog/run-spark-sql-in-fabric-notebooks-without-attaching-a-default-lakehouse</link>
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      <pubDate>Thu, 28 Aug 2025 00:00:00 GMT</pubDate>
      <description><![CDATA[How to keep your Spark jobs portable, safe for concurrency, and easier to orchestrate by using explicit ABFSS paths instead of attaching a default Lakehouse in Microsoft Fabric.]]></description>
    </item>
<item>
      <title><![CDATA[GENERATED BY DEFAULT vs GENERATED ALWAYS in Databricks]]></title>
      <link>https://datadevopslab.pages.dev/blog/generated-by-default-vs-generated-always-in-databricks</link>
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      <pubDate>Tue, 20 May 2025 00:00:00 GMT</pubDate>
      <description><![CDATA[When defining identity columns in Databricks tables, two common options for automatic identity value generation are GENERATED BY DEFAULT and GENERATED ALWAYS. This article explains the differences and when to use each.]]></description>
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<item>
      <title><![CDATA[Synapse Link for Dataverse: Support for Synapse Spark Version 3.4]]></title>
      <link>https://datadevopslab.pages.dev/blog/synapse-link-for-dataverse-support-for-synapse-spark-version-3-4</link>
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      <pubDate>Sun, 19 Jan 2025 00:00:00 GMT</pubDate>
      <description><![CDATA[Microsoft Dynamics suite is a widely used product for implementing ERP and CRM solutions. This article shows how to upgrade Synapse Spark pool version to 3.4 in Synapse Link for Dataverse.]]></description>
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      <title><![CDATA[Export to Datalake vs Synapse Link: Enhancing Data Access in D365 Finance and Operations]]></title>
      <link>https://datadevopslab.pages.dev/blog/export-to-datalake-vs-synapse-link-enhancing-data-access-in-d365-finance-and-operations</link>
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      <pubDate>Fri, 30 Aug 2024 00:00:00 GMT</pubDate>
      <description><![CDATA[Microsoft Dynamics is a widely used product for implementing ERP and CRM solutions. In this article, we will compare export to datalake with synapse link for data...]]></description>
    </item>
<item>
      <title><![CDATA[Use Key Vault Secrets in Azure Data Factory]]></title>
      <link>https://datadevopslab.pages.dev/blog/use-key-vault-secrets-in-azure-data-factory</link>
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      <pubDate>Sun, 14 Jul 2024 00:00:00 GMT</pubDate>
      <description><![CDATA[Azure Data Factory can securely access credentials stored in Azure Key Vault. This article describes how to set up key vault secrets for Azure Data Factory linked services.]]></description>
    </item>
<item>
      <title><![CDATA[Manage Secret Scopes in Databricks]]></title>
      <link>https://datadevopslab.pages.dev/blog/manage-secret-scopes-in-databricks</link>
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      <pubDate>Thu, 11 Jul 2024 00:00:00 GMT</pubDate>
      <description><![CDATA[Databricks can connect to various sources for data ingestion. This article describes how to manage Azure Key Vault-backed secret scopes in Databricks using the GUI.]]></description>
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