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    <title>InfoQ - AI, ML &amp; Data Engineering</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ AI, ML &amp; Data Engineering feed</description>
    <item>
      <title>Article: Understanding ML Model Poisoning: How It Happens and How to Detect It</title>
      <link>https://www.infoq.com/articles/understanding-ml-model-poisoning/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/articles/understanding-ml-model-poisoning/en/headerimage/header-understanding-ml-model-poisoning-1781597719189.jpg"/&gt;&lt;p&gt;In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines.&lt;/p&gt; &lt;i&gt;By Igor Maljkovic&lt;/i&gt;</description>
      <category>Adversarial Machine Learning</category>
      <category>AI Security</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Mon, 22 Jun 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/understanding-ml-model-poisoning/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Igor Maljkovic</dc:creator>
      <dc:date>2026-06-22T11:00:00Z</dc:date>
      <dc:identifier>/articles/understanding-ml-model-poisoning/en</dc:identifier>
    </item>
    <item>
      <title>AWS Graviton5 Reaches General Availability with 192 Cores and Formally Verified VM Isolation</title>
      <link>https://www.infoq.com/news/2026/06/aws-graviton5-ga/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/aws-graviton5-ga/en/headerimage/generatedHeaderImage-1781703289721.jpg"/&gt;&lt;p&gt;AWS made Graviton5-powered EC2 M9g and M9gd instances generally available with 192 ARM cores, formally verified VM isolation via the Nitro Isolation Engine, and DDR5-8800 memory. ClickHouse reported 36% better performance with zero code changes. Meta committed tens of millions of cores. On-demand pricing is 9% above Graviton4, translating to roughly 15% better price-performance.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Containers</category>
      <category>AWS</category>
      <category>AI Architecture</category>
      <category>IaaS</category>
      <category>Cloud Architecture</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 22 Jun 2026 10:05:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/aws-graviton5-ga/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-06-22T10:05:00Z</dc:date>
      <dc:identifier>/news/2026/06/aws-graviton5-ga/en</dc:identifier>
    </item>
    <item>
      <title>Anthropic Reports Claude Now Handles 95% of Internal Analytics Queries</title>
      <link>https://www.infoq.com/news/2026/06/anthropic-claude-analytics/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/anthropic-claude-analytics/en/headerimage/generatedHeaderImage-1781542483302.jpg"/&gt;&lt;p&gt;Anthropic recently reported that Claude now handles around 95% of its internal analytics requests, letting employees query business data independently instead of relying on data teams. The company attributes this result less to advances in models and more to data governance, semantic definitions, and operational discipline.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Data Analytics</category>
      <category>Business Analytics</category>
      <category>Claude</category>
      <category>Data Governance</category>
      <category>Anthropic</category>
      <category>Data Lake</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Sun, 21 Jun 2026 16:47:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/anthropic-claude-analytics/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-06-21T16:47:00Z</dc:date>
      <dc:identifier>/news/2026/06/anthropic-claude-analytics/en</dc:identifier>
    </item>
    <item>
      <title>Apple Launches Core AI for Apple-Silicon Optimized On-Device Generative AI</title>
      <link>https://www.infoq.com/news/2026/06/apple-core-ai-wwdc/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/apple-core-ai-wwdc/en/headerimage/jetbrains-rustrover-ide-1781953134950.jpeg"/&gt;&lt;p&gt;At WWDC 26, Apple announced the Core AI framework, the official successor to Core ML. It is designed to allow developers to run large language models and generative AI entirely on-device, supporting both custom-converted PyTorch models and pre-optimized open-source models.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Mobile</category>
      <category>MacOS</category>
      <category>Apple</category>
      <category>visionOS</category>
      <category>Python</category>
      <category>Artificial Intelligence</category>
      <category>iOS</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Sat, 20 Jun 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/apple-core-ai-wwdc/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-06-20T11:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/apple-core-ai-wwdc/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: AI Agents to Make Sense of Data at OpenAI</title>
      <link>https://www.infoq.com/presentations/data-aware-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/data-aware-ai-agents/en/mediumimage/bonnie-xu-medium-1781164411672.jpg"/&gt;&lt;p&gt;OpenAI’s Bonnie Xu discusses Kepler, an internal AI data analyst agent built to query 600+ petabytes of data. She explains how they overcome context window limits using MCP, automated code crawling, and RAG. Xu also shares how their team leverages scoped semantic memory for self-learning and utilizes AST-based LLM grading to build a robust, regression-free evaluation pipeline.&lt;/p&gt; &lt;i&gt;By Bonnie Xu&lt;/i&gt;</description>
      <category>Agents</category>
      <category>QCon AI 2025</category>
      <category>Artificial Intelligence</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 19 Jun 2026 12:02:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/data-aware-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Bonnie Xu</dc:creator>
      <dc:date>2026-06-19T12:02:00Z</dc:date>
      <dc:identifier>/presentations/data-aware-ai-agents/en</dc:identifier>
    </item>
    <item>
      <title>CircleCI Introduces Chunk Sidecars to Bring CI Validation Directly into AI Coding Workflows</title>
      <link>https://www.infoq.com/news/2026/06/circleci-chunk-sidecars/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/circleci-chunk-sidecars/en/headerimage/generatedHeaderImage-1781604235680.jpg"/&gt;&lt;p&gt;CircleCI has launched Chunk Sidecars, a new capability designed to bring CI-style validation directly into an AI coding agent's inner development loop.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Continuous Integration</category>
      <category>Artificial Intelligence</category>
      <category>Sidecar</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 19 Jun 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/circleci-chunk-sidecars/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-06-19T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/circleci-chunk-sidecars/en</dc:identifier>
    </item>
    <item>
      <title>Azure Functions Ships Serverless Agents Runtime at Build 2026</title>
      <link>https://www.infoq.com/news/2026/06/azure-functions-serverless-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/azure-functions-serverless-agent/en/headerimage/generatedHeaderImage-1781769901887.jpg"/&gt;&lt;p&gt;Azure Functions shipped a serverless agents runtime in public preview at Build 2026. Agents are defined in .agent.md markdown files with YAML triggers, MCP server access, 1,400+ connectors, and sandboxed execution. The Functions team confirmed to InfoQ that the runtime adds no cold start overhead and no billing premium beyond standard Flex Consumption.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Agents</category>
      <category>Microsoft Azure</category>
      <category>AI Architecture</category>
      <category>Azure Functions</category>
      <category>FaaS</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 19 Jun 2026 08:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/azure-functions-serverless-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-06-19T08:57:00Z</dc:date>
      <dc:identifier>/news/2026/06/azure-functions-serverless-agent/en</dc:identifier>
    </item>
    <item>
      <title>Windows Platform Security and the Race to Secure AI Agents</title>
      <link>https://www.infoq.com/news/2026/06/windows-security-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/windows-security-agents/en/headerimage/generatedHeaderImage-1781565951953.jpg"/&gt;&lt;p&gt;In a new Windows Developer Blog post titled "Windows platform security for AI agents", Microsoft positions Windows as the trustworthy operating system for autonomous agents and introduces the Microsoft Execution Containers (MXC) SDK as the core of that strategy. The post argues that containment, identity and manageability must be built into the operating system.&lt;/p&gt; &lt;i&gt;By Matt Saunders&lt;/i&gt;</description>
      <category>Windows</category>
      <category>Agents</category>
      <category>AI Security</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 19 Jun 2026 08:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/windows-security-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Matt Saunders</dc:creator>
      <dc:date>2026-06-19T08:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/windows-security-agents/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Write-Ahead Intent Log: a Foundation for Efficient CDC at Scale</title>
      <link>https://www.infoq.com/presentations/write-ahead-intent-log/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/write-ahead-intent-log/en/mediumimage/vinay-chella-akshat-goel-medium-1781177310280.jpg"/&gt;&lt;p&gt;Vinay Chella and Akshat Goel discuss the challenges of running traditional CDC across heterogeneous databases during peak order traffic. They explain how Debezium hit limits under high load and share how they built Write-Ahead Intent Log (WAIL) - a custom architecture that utilizes a dumb producer proxy and a smart consumer pattern to cleanly separate the intent from the state payload.&lt;/p&gt; &lt;i&gt;By Vinay Chella, Akshat Goel&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>Platform Engineering</category>
      <category>Data Access</category>
      <category>QCon San Francisco 2025</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Thu, 18 Jun 2026 13:13:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/write-ahead-intent-log/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Vinay Chella, Akshat Goel</dc:creator>
      <dc:date>2026-06-18T13:13:00Z</dc:date>
      <dc:identifier>/presentations/write-ahead-intent-log/en</dc:identifier>
    </item>
    <item>
      <title>Microsoft Scout, New  Enterprise Autopilot Built on OpenClaw, Announced at Build 2026</title>
      <link>https://www.infoq.com/news/2026/06/microsoft-scout-openclaw-build/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/microsoft-scout-openclaw-build/en/headerimage/generatedHeaderImage-1781749879309.jpg"/&gt;&lt;p&gt;Microsoft recently introduced at Build 2026 Microsoft Scout, an always-on agent. Scout belongs to a new category of agents Microsoft called Autopilots: always-on agents that work autonomously on a user’s behalf with their own identity, without needing to be prompted each time. Microsoft Scout integrates with Work IQ and is based on the open-source agent framework OpenClaw.&lt;/p&gt; &lt;i&gt;By Bruno Couriol&lt;/i&gt;</description>
      <category>Build</category>
      <category>Agents</category>
      <category>Microsoft</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Thu, 18 Jun 2026 05:26:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/microsoft-scout-openclaw-build/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Bruno Couriol</dc:creator>
      <dc:date>2026-06-18T05:26:00Z</dc:date>
      <dc:identifier>/news/2026/06/microsoft-scout-openclaw-build/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: From Hype to Strong Foundations: What the Rise, Fall and Resurgence of Agents Can Teach Us about Outlasting the Cycle</title>
      <link>https://www.infoq.com/presentations/llm-compound-ai-systems/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/llm-compound-ai-systems/en/mediumimage/medium-1781082183297.jpg"/&gt;&lt;p&gt;Aditya Kumarakrishnan explains how to move past the "amnesia phase" of AI. He shares a blueprint for engineering leaders to build modular agent frameworks using CoALA, leverage decades of process science for scalable workflows, and "terraform" legacy environments into robust, event-sourced artifacts capable of handling unpredictable, cross-functional agent demands.&lt;/p&gt; &lt;i&gt;By Aditya Kumarakrishnan&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
      <category>Model</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 17 Jun 2026 11:04:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/llm-compound-ai-systems/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Aditya Kumarakrishnan</dc:creator>
      <dc:date>2026-06-17T11:04:00Z</dc:date>
      <dc:identifier>/presentations/llm-compound-ai-systems/en</dc:identifier>
    </item>
    <item>
      <title>GitHub Copilot Desktop App Targets Parallel Agentic Workflows</title>
      <link>https://www.infoq.com/news/2026/06/github-copilot-app/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/github-copilot-app/en/headerimage/generatedHeaderImage-1781650188827.jpg"/&gt;&lt;p&gt;GitHub has introduced the GitHub Copilot app, a desktop control centre for agent-native development that aims to keep engineers in charge while AI agents handle more coding work. Mario Rodriguez writes on the GitHub blog that the recent wave of coding agents has brought faster delivery but also "disjointed workflows, more context switching, and too much time spent reviewing agent-generated code".&lt;/p&gt; &lt;i&gt;By Matt Saunders&lt;/i&gt;</description>
      <category>Agents</category>
      <category>copilot</category>
      <category>github</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Wed, 17 Jun 2026 08:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/github-copilot-app/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Matt Saunders</dc:creator>
      <dc:date>2026-06-17T08:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/github-copilot-app/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Automating the Web with MCP: Infra that Doesn’t Break</title>
      <link>https://www.infoq.com/presentations/parallel-agents-production/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/parallel-agents-production/en/mediumimage/paul-klein-medium-1781168002415.jpeg"/&gt;&lt;p&gt;Paul Klein discusses the distributed systems challenges of scaling cloud-hosted browser infra for AI agents.  He explains how to manage bursty, stateful multi-tenancy and secure Chromium environments against remote code execution using Firecracker.  He also shares how to leverage the Model Context Protocol (MCP) to turn complex websites into accessible agentic tools.&lt;/p&gt; &lt;i&gt;By Paul Klein&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Transcripts</category>
      <category>Infrastructure</category>
      <category>QCon San Francisco 2025</category>
      <category>Artificial Intelligence</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>presentation</category>
      <pubDate>Tue, 16 Jun 2026 13:13:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/parallel-agents-production/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Paul Klein</dc:creator>
      <dc:date>2026-06-16T13:13:00Z</dc:date>
      <dc:identifier>/presentations/parallel-agents-production/en</dc:identifier>
    </item>
    <item>
      <title>AI Coding Agents Get a Stack Overflow of Their Own</title>
      <link>https://www.infoq.com/news/2026/06/stack-overflow-for-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/stack-overflow-for-agents/en/headerimage/generatedHeaderImage-1781562336273.jpg"/&gt;&lt;p&gt;Stack Overflow has announced Stack Overflow for Agents, a beta API-first knowledge exchange aimed at AI coding agents rather than human developers. The service is presented as a way to close what the company calls the Ephemeral Intelligence Gap, where agents repeatedly rediscover the same fixes and patterns in isolation instead of sharing them through a common memory.&lt;/p&gt; &lt;i&gt;By Matt Saunders&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Stack Overflow</category>
      <category>Generative AI</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 16 Jun 2026 08:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/stack-overflow-for-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Matt Saunders</dc:creator>
      <dc:date>2026-06-16T08:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/stack-overflow-for-agents/en</dc:identifier>
    </item>
    <item>
      <title>PostgreSQL 19 Beta Introduces SQL Graph Queries and Concurrent Table Repacking</title>
      <link>https://www.infoq.com/news/2026/06/postgresql-19-graph-queries/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/postgresql-19-graph-queries/en/headerimage/generatedHeaderImage-1781180249672.jpg"/&gt;&lt;p&gt;PostgreSQL 19 Beta has been announced, with general availability expected in September, following the project's yearly major-release cadence. This release introduces native SQL Property Graph Queries (SQL/PGQ), concurrent table repacking to reclaim storage without downtime, and a broad set of performance, observability, and administration improvements.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Postgres</category>
      <category>SQL</category>
      <category>Relational Databases</category>
      <category>Database Replication</category>
      <category>Graph Database</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 16 Jun 2026 07:15:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/postgresql-19-graph-queries/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-06-16T07:15:00Z</dc:date>
      <dc:identifier>/news/2026/06/postgresql-19-graph-queries/en</dc:identifier>
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