<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ByteLoom Consulting — Technical Insights</title><description>Technical articles on Databricks lakehouse engineering, data platform modernization, governance, Spark performance and production AI engineering.</description><link>https://byteloomconsulting.com/insights/</link><language>en</language><atom:link href="https://byteloomconsulting.com/rss.xml" rel="self" type="application/rss+xml"/><item><title>Building an Enterprise AI Observability Framework</title><link>https://byteloomconsulting.com/insights/enterprise-ai-observability-framework/</link><guid isPermaLink="true">https://byteloomconsulting.com/insights/enterprise-ai-observability-framework/</guid><description>A reference framework for observing LLM and RAG applications: signals, a Delta telemetry model, cost attribution, evaluation and governance on Databricks.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate><category>AI Engineering</category><category>ai-observability</category><category>llm</category><category>rag</category><category>mlflow</category><category>opentelemetry</category><category>llm-evaluation</category><category>databricks</category></item><item><title>Designing Enterprise Data Governance with Unity Catalog</title><link>https://byteloomconsulting.com/insights/unity-catalog-enterprise-data-governance/</link><guid isPermaLink="true">https://byteloomconsulting.com/insights/unity-catalog-enterprise-data-governance/</guid><description>Designing Unity Catalog for an enterprise: catalogs, workspace binding, group-based grants, ABAC, lineage, grants as code and Hive metastore migration.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate><category>Data Governance</category><category>unity-catalog</category><category>databricks</category><category>data-governance</category><category>access-control</category><category>abac</category><category>terraform</category></item><item><title>Migrating Legacy ETL Pipelines to Databricks: Architecture and Validation</title><link>https://byteloomconsulting.com/insights/migrating-legacy-etl-to-databricks/</link><guid isPermaLink="true">https://byteloomconsulting.com/insights/migrating-legacy-etl-to-databricks/</guid><description>Triage a Talend, Informatica or SSIS estate, design the lakehouse target, translate ETL semantics, and prove the result with a reconciliation framework.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate><category>Data Engineering</category><category>databricks</category><category>etl-migration</category><category>lakehouse</category><category>data-validation</category><category>delta-lake</category><category>lakeflow</category><category>pyspark</category></item><item><title>Optimizing Databricks Compute Costs and Spark Workloads</title><link>https://byteloomconsulting.com/insights/optimizing-databricks-compute-costs-spark-workloads/</link><guid isPermaLink="true">https://byteloomconsulting.com/insights/optimizing-databricks-compute-costs-spark-workloads/</guid><description>Attribute Databricks spend with system tables, choose compute per workload, tune Spark and Delta layout, and hold the savings with policies and reviews.</description><pubDate>Tue, 10 Nov 2026 00:00:00 GMT</pubDate><category>Performance &amp; Cost</category><category>databricks</category><category>spark</category><category>cost-optimization</category><category>finops</category><category>delta-lake</category><category>system-tables</category></item></channel></rss>