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    <title>BricksNotes Journal</title>
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    <description>Practical writing on Databricks, Spark, Delta Lake, and the craft of data engineering.</description>
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    <lastBuildDate>Thu, 08 Oct 2026 01:34:24 GMT</lastBuildDate>
    <item>
      <title>The Future of Data to Decisions Is Autonomous</title>
      <link>https://bricksnotes.com/blog/future-of-data-to-decisions-is-autonomous</link>
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      <pubDate>Thu, 08 Oct 2026 01:34:24 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Dashboards wait for people. Autonomous systems act on trusted data within clear limits. Here is what that future looks like, five use cases, and a Free Edition lab to build your first decision loop.</description>
      <category>AI Agents</category>
      <category>Data Engineering</category>
      <category>Databricks</category>
      <category>Future of Data</category>
      <category>Context Engineering</category>
    </item>
    <item>
      <title>Your Databricks Job Took 42 Minutes Instead of 8. What Changed?</title>
      <link>https://bricksnotes.com/blog/debug-slow-spark-stage-databricks</link>
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      <pubDate>Wed, 07 Oct 2026 01:37:02 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>When a Spark job suddenly slows down, reading the code is rarely enough. Learn to find the slowest stage, read the plan, spot data skew in the task chart, and choose the right fix.</description>
      <category>Spark</category>
      <category>Performance</category>
      <category>Databricks</category>
      <category>Debugging</category>
      <category>Data Engineering</category>
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    <item>
      <title>Every Data Engineer Faces These 50 Problems. The Best Engineers Know the Solutions.</title>
      <link>https://bricksnotes.com/blog/data-engineering-50-problems-solutions</link>
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      <pubDate>Tue, 06 Oct 2026 02:07:42 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Duplicates, skew, small files, schema changes, late data, slow joins, high costs. Most production issues repeat. This field guide pairs 50 real problems with the solution, the reason it works, and the chapter where you can practice it.</description>
      <category>Data Engineering</category>
      <category>Databricks</category>
      <category>Performance</category>
      <category>Data Quality</category>
      <category>Best Practices</category>
    </item>
    <item>
      <title>Databricks ai_decide Explained: Fast AI Decisions in SQL</title>
      <link>https://bricksnotes.com/blog/databricks-ai-decide-explained</link>
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      <pubDate>Sun, 04 Oct 2026 15:19:13 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>ai_decide is a new Databricks AI Function that answers choice questions with probabilities, not text. Learn its three question types, real SQL patterns and how to route by confidence.</description>
      <category>AI and data</category>
      <category>Databricks SQL</category>
      <category>context-advantage</category>
    </item>
    <item>
      <title>You have a model. Now what context does it need?</title>
      <link>https://bricksnotes.com/blog/context-engineering-model-tools-guardrails</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/context-engineering-model-tools-guardrails</guid>
      <pubDate>Sun, 04 Oct 2026 01:46:27 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>A model alone is not an agent. The choices around it, context, tools, instructions and guardrails, decide how useful it is and how much control the user keeps.</description>
      <category>AI and data</category>
      <category>context-advantage</category>
      <category>Agents</category>
    </item>
    <item>
      <title>Databricks Genie Explained: The AI Analyst You Have to Onboard</title>
      <link>https://bricksnotes.com/blog/databricks-genie-explained-ai-analyst</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-genie-explained-ai-analyst</guid>
      <pubDate>Fri, 02 Oct 2026 03:43:32 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Databricks Genie turns business questions into SQL on your warehouse. The SQL is rarely the hard part. The hard part is teaching Genie what your business terms mean, the same way you would onboard a new analyst.</description>
      <category>AI and data</category>
      <category>context-advantage</category>
      <category>Genie</category>
    </item>
    <item>
      <title>What Happens When Two AI Agents Disagree?</title>
      <link>https://bricksnotes.com/blog/when-two-ai-agents-disagree</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/when-two-ai-agents-disagree</guid>
      <pubDate>Thu, 01 Oct 2026 12:30:00 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>One agent says approve. The other says reject. Both followed their instructions. What decides the outcome is not intelligence, it is shared context, clear authority, and a safe way to escalate to a human. A practical look at multi-agent system design, with a refund example and a Databricks Free Edition exercise.</description>
      <category>AI and data</category>
      <category>context-advantage</category>
    </item>
    <item>
      <title>Superintelligence Is Coming. The Foundation Will Still Be Data.</title>
      <link>https://bricksnotes.com/blog/superintelligence-foundation-is-data</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/superintelligence-foundation-is-data</guid>
      <pubDate>Wed, 30 Sep 2026 13:47:49 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Experts have stopped saying AI and started saying superintelligence. The name changed. The dependency did not. Whoever owns trusted, well defined, governed data still decides how useful intelligent systems become. A long read for data engineers, analysts and everyone who keeps the numbers honest.</description>
      <category>AI and data</category>
      <category>context-advantage</category>
    </item>
    <item>
      <title>Databricks Acquires Row Zero: Live, Governed Spreadsheets Come to Genie</title>
      <link>https://bricksnotes.com/blog/databricks-row-zero-genie-spreadsheets</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-row-zero-genie-spreadsheets</guid>
      <pubDate>Wed, 30 Sep 2026 06:30:00 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Databricks acquired Row Zero to end the spreadsheet export. Here is what live governed spreadsheets change for finance, analysts and AI agents.</description>
      <category>Genie</category>
      <category>Governance</category>
      <category>Unity Catalog</category>
      <category>The Context Advantage</category>
      <category>Spreadsheets</category>
      <category>Data + AI updates</category>
    </item>
    <item>
      <title>AI Answers Fast. Data Engineering Decides if You Can Trust It.</title>
      <link>https://bricksnotes.com/blog/ai-answers-fast-data-engineering-trust</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/ai-answers-fast-data-engineering-trust</guid>
      <pubDate>Tue, 29 Sep 2026 02:00:00 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>An AI assistant can answer in one second. Whether that answer deserves trust is decided long before the question is asked, by six basics of data engineering.</description>
      <category>Data + AI Updates</category>
      <category>Data Quality</category>
      <category>Governance</category>
      <category>Context Advantage</category>
      <category>Career</category>
    </item>
    <item>
      <title>Your Spreadsheet Is Becoming an AI Interface. That Changes Who Owns the Numbers.</title>
      <link>https://bricksnotes.com/blog/governed-ai-spreadsheets-trusted-metrics</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/governed-ai-spreadsheets-trusted-metrics</guid>
      <pubDate>Mon, 28 Sep 2026 02:00:00 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Three spreadsheets, three revenue numbers, zero mistakes. Why AI makes shared definitions matter more, and how to practice it in Free Edition.</description>
      <category>Genie</category>
      <category>Governance</category>
      <category>Semantic Layer</category>
      <category>Data + AI Updates</category>
      <category>Unity Catalog</category>
      <category>Context Advantage</category>
    </item>
    <item>
      <title>When AI Agents Reach Trillion-Token Scale, the Architecture Changes</title>
      <link>https://bricksnotes.com/blog/when-ai-agents-reach-trillion-token-scale</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/when-ai-agents-reach-trillion-token-scale</guid>
      <pubDate>Thu, 24 Sep 2026 01:10:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>What a trillion-token clinical AI system teaches data teams about trusted context, durable state, controlled access, observability, and cost.</description>
      <category>AI agents</category>
      <category>Unity Gateway</category>
      <category>Lakebase</category>
      <category>Observability</category>
      <category>The Context Advantage</category>
      <category>Data engineering</category>
    </item>
    <item>
      <title>Genie One MCP: One Business Language Across Every AI Assistant</title>
      <link>https://bricksnotes.com/blog/genie-one-mcp-enterprise-context</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/genie-one-mcp-enterprise-context</guid>
      <pubDate>Wed, 23 Sep 2026 01:04:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>Learn how Genie One MCP connects different AI assistants to shared business meaning, richer analytical results, and governed access through Unity Gateway.</description>
      <category>Genie One</category>
      <category>MCP</category>
      <category>enterprise context</category>
      <category>AI agents</category>
      <category>Unity Gateway</category>
      <category>Genie Ontology</category>
    </item>
    <item>
      <title>Enterprise Context: Why AI Needs It Before It Needs Smarter Models</title>
      <link>https://bricksnotes.com/blog/enterprise-context-ai-agents</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/enterprise-context-ai-agents</guid>
      <pubDate>Tue, 22 Sep 2026 01:27:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>Ali Ghodsi says enterprise context is the missing piece between chatbots and autonomous agents. Learn the architecture, controls, and practical steps behind it.</description>
      <category>enterprise AI</category>
      <category>context engineering</category>
      <category>AI agents</category>
      <category>Genie Ontology</category>
      <category>semantic understanding</category>
    </item>
    <item>
      <title>Every dashboard was green. Customers were still failing.</title>
      <link>https://bricksnotes.com/blog/gray-failures-anomaly-detection-databricks</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/gray-failures-anomaly-detection-databricks</guid>
      <pubDate>Sun, 20 Sep 2026 17:00:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>Gray failures slip past healthy dashboards and quietly cost you customers. Learn the four-stage RADAR pattern Databricks uses, and try the core idea in Free Edition.</description>
      <category>observability</category>
      <category>anomaly-detection</category>
      <category>reliability</category>
      <category>data-engineering</category>
    </item>
    <item>
      <title>SQL just learned to find patterns: MATCH_RECOGNIZE, explained simply</title>
      <link>https://bricksnotes.com/blog/match-recognize-sql-pattern-matching-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/match-recognize-sql-pattern-matching-explained</guid>
      <pubDate>Sat, 19 Sep 2026 13:00:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>MATCH_RECOGNIZE is a new Databricks SQL operator that finds event sequences, like a regular expression for rows. Learn the pattern syntax with runnable examples.</description>
      <category>SQL</category>
      <category>Databricks</category>
      <category>Spark SQL</category>
      <category>Analytics</category>
    </item>
    <item>
      <title>Your AI agent does not have a semantic problem. Your tables do.</title>
      <link>https://bricksnotes.com/blog/enterprise-ai-semantic-understanding-data-model</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/enterprise-ai-semantic-understanding-data-model</guid>
      <pubDate>Fri, 18 Sep 2026 01:20:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>Enterprise AI does not need another pile of data. It needs shared business definitions that agents can inspect, explain, and use safely.</description>
      <category>Enterprise AI</category>
      <category>Semantic Layer</category>
      <category>Data Modeling</category>
      <category>Databricks</category>
      <category>AI Agents</category>
      <category>Governance</category>
    </item>
    <item>
      <title>The checkpoint trap is finally fixable: resize stateful Spark streams without starting over</title>
      <link>https://bricksnotes.com/blog/spark-structured-streaming-state-repartitioning</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/spark-structured-streaming-state-repartitioning</guid>
      <pubDate>Thu, 17 Sep 2026 02:20:32 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>A practical guide to on-demand state repartitioning in Apache Spark Structured Streaming on Databricks, including the checkpoint trap, RocksDB requirements, code, monitoring, and a safe rollout plan.</description>
      <category>Databricks</category>
      <category>Apache Spark</category>
      <category>Structured Streaming</category>
      <category>RocksDB</category>
      <category>Performance</category>
    </item>
    <item>
      <title>The answer is only the beginning: how shared context turns insight into action</title>
      <link>https://bricksnotes.com/blog/shared-context-to-trusted-action-genie-ontology</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/shared-context-to-trusted-action-genie-ontology</guid>
      <pubDate>Wed, 16 Sep 2026 02:32:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>Why Genie One and Genie Ontology matter beyond question answering, and how shared context can shorten the path from a business signal to a trusted action.</description>
      <category>Genie</category>
      <category>AI Agents</category>
      <category>Context Engineering</category>
      <category>Governance</category>
      <category>Databricks</category>
    </item>
    <item>
      <title>Genie One, explained simply: the AI coworker that already knows your data</title>
      <link>https://bricksnotes.com/blog/databricks-genie-one-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-genie-one-explained</guid>
      <pubDate>Mon, 14 Sep 2026 14:35:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>Genie One is not a smarter search box. It is an AI coworker that reads your governed data, drafts the SQL, and can take the next step. Here is how it works, and what still belongs to you.</description>
      <category>genie</category>
      <category>ai</category>
      <category>databricks</category>
      <category>governance</category>
      <category>sql</category>
    </item>
    <item>
      <title>Your AI agent remembers the refund. The business still says pending.</title>
      <link>https://bricksnotes.com/blog/ai-agent-memory-vs-business-state</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/ai-agent-memory-vs-business-state</guid>
      <pubDate>Sun, 13 Sep 2026 22:55:00 GMT</pubDate>
      <dc:creator>Team BricksNotes</dc:creator>
      <description>An AI agent can remember a promise without making it true. Here is how systems of record, governed state changes, and audit trails keep agent actions reliable.</description>
      <category>AI Agents</category>
      <category>Data Engineering</category>
      <category>Architecture</category>
      <category>Governance</category>
      <category>Databricks</category>
    </item>
    <item>
      <title>You partitioned the table. It got slower. Partitioning and liquid clustering, explained.</title>
      <link>https://bricksnotes.com/blog/liquid-clustering-vs-partitioning-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/liquid-clustering-vs-partitioning-explained</guid>
      <pubDate>Sun, 13 Sep 2026 04:33:23 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Partitioning feels like the obvious answer until the folders multiply and queries slow down. Here is what is really happening, and what liquid clustering does differently.</description>
      <category>databricks</category>
      <category>delta-lake</category>
      <category>performance</category>
      <category>liquid-clustering</category>
      <category>data-engineering</category>
    </item>
    <item>
      <title>One agent is easy. Managing a fleet of them is the real job.</title>
      <link>https://bricksnotes.com/blog/managing-ai-agent-fleet-choice-context-control</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/managing-ai-agent-fleet-choice-context-control</guid>
      <pubDate>Sat, 12 Sep 2026 03:57:37 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Enterprises are adopting multiple AI coding tools at once. Here is a calm, practical way to think about managing that fleet: choice of models, shared governed context, and one control plane.</description>
      <category>ai-agents</category>
      <category>governance</category>
      <category>context-advantage</category>
      <category>databricks</category>
      <category>enterprise-ai</category>
    </item>
    <item>
      <title>The customer moved cities. Slowly changing dimensions, explained simply</title>
      <link>https://bricksnotes.com/blog/slowly-changing-dimensions-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/slowly-changing-dimensions-explained</guid>
      <pubDate>Fri, 11 Sep 2026 01:37:54 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>A customer moved from Pune to Mumbai. Simple in life, quietly difficult in data. What your table chooses to forget decides what your reports can prove.</description>
      <category>Data Modeling</category>
      <category>Delta Lake</category>
      <category>Fundamentals</category>
    </item>
    <item>
      <title>Incremental Processing Explained: Do Only the Work That Is Necessary</title>
      <link>https://bricksnotes.com/blog/incremental-processing-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/incremental-processing-explained</guid>
      <pubDate>Thu, 10 Sep 2026 02:21:09 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>You have a billion rows and a million new ones arrive daily. Would you reprocess everything? This explainer covers change detection, merge (upsert) logic, late-arriving data, overlap windows, idempotency, checkpoints, and how it maps to Bronze, Silver, and Gold.</description>
      <category>data-engineering</category>
      <category>databricks</category>
      <category>delta-lake</category>
      <category>fundamentals</category>
    </item>
    <item>
      <title>30,000 Practice Exams. Thank You.</title>
      <link>https://bricksnotes.com/blog/30000-practice-exams-bricksnotes-milestone</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/30000-practice-exams-bricksnotes-milestone</guid>
      <pubDate>Wed, 09 Sep 2026 03:02:55 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>More than 30,000 practice exams have been completed on BricksNotes in the last 9 months. Thank you to every learner preparing quietly, one concept at a time.</description>
      <category>Community</category>
      <category>Milestone</category>
    </item>
    <item>
      <title>Data and AI in space exploration: from orbit to answer</title>
      <link>https://bricksnotes.com/blog/data-ai-space-exploration</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/data-ai-space-exploration</guid>
      <pubDate>Wed, 09 Sep 2026 01:28:01 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Space missions are data missions. Here is how telemetry, science payloads, versioned history and onboard models fit together, and how a lakehouse supports all of it.</description>
      <category>data engineering</category>
      <category>AI</category>
      <category>space</category>
      <category>vision</category>
    </item>
    <item>
      <title>The small files problem in Spark and Databricks, explained simply</title>
      <link>https://bricksnotes.com/blog/small-files-problem-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/small-files-problem-explained</guid>
      <pubDate>Tue, 08 Sep 2026 02:53:30 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>A pipeline can process the correct data and still be painfully slow. The cause is often hundreds of thousands of tiny files. Learn why the small files problem happens, how to spot it, and how to fix it with partitioning, OPTIMIZE, and better write habits.</description>
      <category>spark</category>
      <category>databricks</category>
      <category>delta lake</category>
      <category>performance</category>
    </item>
    <item>
      <title>Your next assistant is an agent. And it runs on data</title>
      <link>https://bricksnotes.com/blog/personal-ai-agents-2026-data-future</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/personal-ai-agents-2026-data-future</guid>
      <pubDate>Mon, 07 Sep 2026 03:36:36 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Personal AI agents are coming fast, and they all run on trustworthy data. What the agent era means for data engineering careers, and how to start building toward it today.</description>
      <category>AI</category>
      <category>data engineering</category>
      <category>agents</category>
      <category>career</category>
      <category>vision</category>
    </item>
    <item>
      <title>Medallion architecture explained: bronze, silver, and gold without the jargon</title>
      <link>https://bricksnotes.com/blog/medallion-architecture-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/medallion-architecture-explained</guid>
      <pubDate>Sun, 06 Sep 2026 02:59:34 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Medallion architecture explained in plain words: what bronze, silver, and gold tables are really for, a worked example, the mistakes beginners make, and when fewer layers are the better design.</description>
      <category>databricks</category>
      <category>delta lake</category>
      <category>medallion architecture</category>
      <category>fundamentals</category>
    </item>
    <item>
      <title>Data Agents and the Next Decade of Data Engineering</title>
      <link>https://bricksnotes.com/blog/data-agents-future-of-data-engineering</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/data-agents-future-of-data-engineering</guid>
      <pubDate>Sat, 05 Sep 2026 05:26:55 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>AI agents are starting to work with data directly. This article looks at what data agents are, why trustworthy tables matter more than ever, and how a beginner today can prepare for the agent era.</description>
      <category>AI</category>
      <category>Data Engineering</category>
      <category>Vision</category>
      <category>Agents</category>
    </item>
    <item>
      <title>The future belongs to data and AI. A golden era is opening up for the people who build it</title>
      <link>https://bricksnotes.com/blog/data-ai-golden-era-vision</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/data-ai-golden-era-vision</guid>
      <pubDate>Fri, 04 Sep 2026 17:10:12 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>The future belongs to data and AI, and the bottleneck is no longer the model. It is trustworthy data. A look at why this era rewards builders, the honest skill path from foundations to AI readiness, and the BricksNotes vision behind it.</description>
      <category>data engineering</category>
      <category>AI</category>
      <category>vision</category>
      <category>career</category>
    </item>
    <item>
      <title>The next phase of data engineering is not faster pipelines. It is faster decisions</title>
      <link>https://bricksnotes.com/blog/data-engineering-faster-decisions</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/data-engineering-faster-decisions</guid>
      <pubDate>Thu, 03 Sep 2026 02:11:09 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Your pipeline runs in four minutes and the decision still waits for Monday. A practical look at decision latency, designing backwards from the decision, and what changes when the consumer is an agent.</description>
      <category>data engineering</category>
      <category>decisions</category>
      <category>databricks</category>
    </item>
    <item>
      <title>The table had 40,000 tiny files. The small file problem in Databricks, explained</title>
      <link>https://bricksnotes.com/blog/databricks-small-file-problem-optimize-vacuum</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-small-file-problem-optimize-vacuum</guid>
      <pubDate>Wed, 02 Sep 2026 03:10:00 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>A Delta table that was fast in week one became slow by month three. The data had not grown much. The file count had. Here is what the small file problem is, why streaming and frequent merges cause it, and how OPTIMIZE and VACUUM keep a table healthy.</description>
      <category>databricks</category>
      <category>delta lake</category>
      <category>performance</category>
      <category>fundamentals</category>
    </item>
    <item>
      <title>What did this table look like last Tuesday? Delta Lake time travel, explained</title>
      <link>https://bricksnotes.com/blog/delta-lake-time-travel-explained</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/delta-lake-time-travel-explained</guid>
      <pubDate>Tue, 01 Sep 2026 03:38:53 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Delta Lake keeps every version of your table. Learn how to query older versions with VERSION AS OF, read table history, and undo bad writes with RESTORE, with simple examples that run in Databricks Free Edition.</description>
      <category>Databricks</category>
      <category>Delta Lake</category>
      <category>time travel</category>
      <category>data engineering</category>
    </item>
    <item>
      <title>The dashboard was wrong before anyone knew. Observability in a Databricks pipeline</title>
      <link>https://bricksnotes.com/blog/databricks-pipeline-observability-alerts-runbook</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-pipeline-observability-alerts-runbook</guid>
      <pubDate>Mon, 31 Aug 2026 01:51:24 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>A green run is a weak signal. Here are the four questions your pipeline should answer on its own, with freshness checks, run metrics, Delta constraints and alerts you can build in Free Edition.</description>
      <category>Databricks</category>
      <category>monitoring</category>
      <category>data-quality</category>
      <category>pipelines</category>
    </item>
    <item>
      <title>Databricks acquired Electric. Why data next to the agent matters</title>
      <link>https://bricksnotes.com/blog/databricks-electric-acquisition-lakebase-agents</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-electric-acquisition-lakebase-agents</guid>
      <pubDate>Sun, 30 Aug 2026 02:25:52 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Databricks acquiring Electric is a bet that agents need data close to where they run. Here is what it means for Lakebase, and what it changes for data engineers.</description>
      <category>Databricks</category>
      <category>ai-agents</category>
      <category>lakebase</category>
      <category>architecture</category>
    </item>
    <item>
      <title>Lakebase, explained simply. Where it came from and what to build with it</title>
      <link>https://bricksnotes.com/blog/what-is-lakebase-explained-simply</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/what-is-lakebase-explained-simply</guid>
      <pubDate>Sat, 29 Aug 2026 02:56:00 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Lakebase is managed Postgres inside Databricks. Here is the idea behind it, where it fits, the use cases worth trying, and why agents make it more than a convenience feature.</description>
      <category>Databricks</category>
      <category>Lakebase</category>
      <category>Data + AI</category>
      <category>Architecture</category>
      <category>Agents</category>
    </item>
    <item>
      <title>Same result, 3 seconds or 30 minutes. Query optimization in Databricks</title>
      <link>https://bricksnotes.com/blog/databricks-query-optimization-execution-plan</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-query-optimization-execution-plan</guid>
      <pubDate>Fri, 28 Aug 2026 01:42:09 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Two queries, identical results, wildly different cost. Here is what happens between the SQL you write and the answer you get, and the seven techniques that decide how much work the engine does.</description>
      <category>Databricks</category>
      <category>Spark</category>
      <category>Performance</category>
      <category>SQL</category>
      <category>Data Engineering</category>
    </item>
    <item>
      <title>The join that ran for seven hours. Broadcast and shuffle joins in Databricks</title>
      <link>https://bricksnotes.com/blog/databricks-broadcast-vs-shuffle-joins</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-broadcast-vs-shuffle-joins</guid>
      <pubDate>Thu, 27 Aug 2026 02:09:50 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>One line joined orders to customers, and an eleven minute job ran all night. Here is what a join really does on a cluster, and how to choose the right one.</description>
      <category>Databricks</category>
      <category>Spark</category>
      <category>Performance</category>
      <category>Data Engineering</category>
    </item>
    <item>
      <title>The pipeline kept running. Why silent schema changes are the dangerous ones</title>
      <link>https://bricksnotes.com/blog/schema-changes-data-contracts-databricks</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/schema-changes-data-contracts-databricks</guid>
      <pubDate>Wed, 26 Aug 2026 01:34:12 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>Schema evolution is normal. Uncontrolled schema evolution is dangerous. The five change types, why a data contract does what a schema cannot, and six habits that make change visible, testable and safe in Databricks.</description>
      <category>Databricks</category>
      <category>Schema Evolution</category>
      <category>Data Quality</category>
      <category>Data Engineering</category>
    </item>
    <item>
      <title>The order was counted twice. Duplicate records in Databricks, explained</title>
      <link>https://bricksnotes.com/blog/databricks-duplicate-records-deduplication</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-duplicate-records-deduplication</guid>
      <pubDate>Tue, 25 Aug 2026 00:34:08 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>A pipeline can finish green and your numbers can still be wrong. Duplicates rarely break pipelines. They break business decisions. Here is how to handle them properly.</description>
      <category>Databricks</category>
      <category>Data Quality</category>
      <category>SQL</category>
      <category>Fundamentals</category>
    </item>
    <item>
      <title>The next database may be built as much for agents as for developers</title>
      <link>https://bricksnotes.com/blog/databases-built-for-agents-not-just-developers</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databases-built-for-agents-not-just-developers</guid>
      <pubDate>Mon, 24 Aug 2026 00:53:14 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>For forty years databases were designed around a human who could ask a teammate. Agents cannot. Here is how a data platform changes when machines are the main readers, and what to do about it in Databricks this week.</description>
      <category>AI Agents</category>
      <category>Unity Catalog</category>
      <category>Data Engineering</category>
      <category>Databricks</category>
    </item>
    <item>
      <title>One task ran for forty minutes. Data skew in Databricks, explained</title>
      <link>https://bricksnotes.com/blog/databricks-data-skew-slow-tasks</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-data-skew-slow-tasks</guid>
      <pubDate>Sun, 23 Aug 2026 03:34:15 GMT</pubDate>
      <dc:creator>BricksNotes</dc:creator>
      <description>The code did not change. The data did. Here is how one heavy key turns a nine minute job into a fifty one minute one, and how to fix it.</description>
      <category>Databricks</category>
      <category>Spark</category>
      <category>Performance</category>
      <category>Joins</category>
    </item>
    <item>
      <title>CSV, JSON, Parquet, and Delta: which one should you use?</title>
      <link>https://bricksnotes.com/blog/csv-json-parquet-delta-which-format-to-use</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/csv-json-parquet-delta-which-format-to-use</guid>
      <pubDate>Sat, 22 Aug 2026 15:16:16 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>CSV, JSON, Parquet, or Delta? A practical comparison with runnable Databricks Free Edition examples, real size and speed differences, and a simple rule for each layer of your pipeline.</description>
      <category>Delta Lake</category>
      <category>Parquet</category>
      <category>File Formats</category>
      <category>Data Engineering</category>
    </item>
    <item>
      <title>How BricksNotes went from 40,000 to 100,000 learners</title>
      <link>https://bricksnotes.com/blog/bricksnotes-40000-to-100000-learners</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/bricksnotes-40000-to-100000-learners</guid>
      <pubDate>Fri, 21 Aug 2026 23:57:32 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>A hundred thousand data professionals have now learned with BricksNotes. Here is the honest story of how it happened, what we built along the way, and why we are not stopping.</description>
      <category>Community</category>
      <category>Milestone</category>
      <category>Data Engineering</category>
      <category>2026</category>
    </item>
    <item>
      <title>The pipeline was green. The numbers were wrong.</title>
      <link>https://bricksnotes.com/blog/databricks-data-quality-checks</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-data-quality-checks</guid>
      <pubDate>Fri, 21 Aug 2026 02:21:49 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>A successful run tells you the code finished, not that the data is right. How to write quality rules, decide what happens to bad rows, and enforce them with Delta constraints and pipeline expectations.</description>
      <category>Databricks</category>
      <category>Data Quality</category>
      <category>Delta Lake</category>
      <category>Pipelines</category>
    </item>
    <item>
      <title>I built a data engineering project in two evenings. Here is exactly what I did</title>
      <link>https://bricksnotes.com/blog/built-first-data-engineering-project-two-evenings</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/built-first-data-engineering-project-two-evenings</guid>
      <pubDate>Thu, 20 Aug 2026 13:30:43 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>A boundary instead of a study plan: two evenings, 100 messy orders, one gold table, and a pipeline that runs twice without breaking. Here is the full walkthrough you can repeat tonight in Databricks Free Edition.</description>
      <category>Databricks</category>
      <category>Data Engineering</category>
      <category>PySpark</category>
      <category>Getting Started</category>
      <category>2026</category>
    </item>
    <item>
      <title>The event arrived two days late. How to handle late data in Databricks</title>
      <link>https://bricksnotes.com/blog/databricks-late-arriving-data</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-late-arriving-data</guid>
      <pubDate>Thu, 20 Aug 2026 01:50:50 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>A phone was offline, a partner file was late, a queue backed up. Here is how to keep event time honest and stop numbers from drifting quietly.</description>
      <category>Databricks</category>
      <category>Streaming</category>
      <category>Delta Lake</category>
      <category>Data Quality</category>
    </item>
    <item>
      <title>The backfill. How to reload history in Databricks without breaking downstream tables</title>
      <link>https://bricksnotes.com/blog/databricks-backfill-without-breaking-downstream</link>
      <guid isPermaLink="true">https://bricksnotes.com/blog/databricks-backfill-without-breaking-downstream</guid>
      <pubDate>Wed, 19 Aug 2026 01:51:14 GMT</pubDate>
      <dc:creator>BricksNotes Editorial</dc:creator>
      <description>Reloading March means touching a table six dashboards already read. Here is the plan that makes a backfill boring instead of frightening.</description>
      <category>Databricks</category>
      <category>Delta Lake</category>
      <category>Backfill</category>
      <category>Pipelines</category>
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