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    <title>Modern DataTools Blog</title>
    <link>https://www.modern-datatools.com/blog</link>
    <description>Market signals, technology-decision frameworks, architecture research, and practical lessons from Modern DataTools</description>
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    <lastBuildDate>Sat, 08 Aug 2026 08:25:05 GMT</lastBuildDate>
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    <item>
      <title>Data Tool Signals: July 2026 — Package Gains, Steady Infrastructure</title>
      <link>https://www.modern-datatools.com/blog/modern-datatools-signal-report-july-2026</link>
      <guid>https://www.modern-datatools.com/blog/modern-datatools-signal-report-july-2026</guid>
      <pubDate>Sat, 08 Aug 2026 08:00:55 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>A July read on developer packages, mature infrastructure scale, and the reader attention worth watching.</description>
    </item>
    <item>
      <title>Caching Lessons from Modern DataTools: Keeping Pages Fast and Fresh</title>
      <link>https://www.modern-datatools.com/blog/caching-lessons-part1</link>
      <guid>https://www.modern-datatools.com/blog/caching-lessons-part1</guid>
      <pubDate>Fri, 31 Jul 2026 16:23:56 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Modern DataTools uses page caching, targeted invalidation, and shared data caches to reduce repeated work without allowing important information to remain outdated - in the article we cover our key lessons for these mechanisms.</description>
    </item>
    <item>
      <title>A Few Things I&apos;ve Learned About AI-Assisted Development in Mid-2026</title>
      <link>https://www.modern-datatools.com/blog/ai-assisted-software-development-2026</link>
      <guid>https://www.modern-datatools.com/blog/ai-assisted-software-development-2026</guid>
      <pubDate>Tue, 21 Jul 2026 19:22:12 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Practical lessons from using coding agents on a growing codebase: repository rules, changelogs, scripts, testing, and context.</description>
    </item>
    <item>
      <title>Modern DataTools: The Data Layer for Better Technology Decisions</title>
      <link>https://www.modern-datatools.com/blog/modern-datatools-the-data-layer-for-better-technology-decisions</link>
      <guid>https://www.modern-datatools.com/blog/modern-datatools-the-data-layer-for-better-technology-decisions</guid>
      <pubDate>Wed, 15 Jul 2026 19:26:30 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Modern DataTools turns continuously collected data about 300 data and AI tools into comparisons, market intelligence, and evidence-backed stack recommendations.</description>
    </item>
    <item>
      <title>Vector Databases Explained: When You Actually Need One (and When You Don&apos;t)</title>
      <link>https://www.modern-datatools.com/blog/vector-databases-when-you-need-one</link>
      <guid>https://www.modern-datatools.com/blog/vector-databases-when-you-need-one</guid>
      <pubDate>Fri, 10 Jul 2026 12:37:13 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>The vector database gold rush is in full swing. But for most teams, pgvector is all you need. Here&apos;s how to decide.</description>
    </item>
    <item>
      <title>Modern DataTools Signal Report: June 2026</title>
      <link>https://www.modern-datatools.com/blog/modern-datatools-signal-report-june-2026</link>
      <guid>https://www.modern-datatools.com/blog/modern-datatools-signal-report-june-2026</guid>
      <pubDate>Wed, 01 Jul 2026 07:07:47 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Monthly analysis of the data tools gaining adoption, community interest, and reader momentum.</description>
    </item>
    <item>
      <title>Redesigning the Stack Recommender: From Static Suggestions to Interactive Architecture Decisions</title>
      <link>https://www.modern-datatools.com/blog/stack-recommender-redesign-2026</link>
      <guid>https://www.modern-datatools.com/blog/stack-recommender-redesign-2026</guid>
      <pubDate>Mon, 29 Jun 2026 10:19:06 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>How Modern DataTools redesigned Stack Recommender with interactive customization, shareable URLs, evidence quality, integration visibility, and clearer methodology.</description>
    </item>
    <item>
      <title>AI Agents for Data Teams: Separating Hype from Production-Ready Tools</title>
      <link>https://www.modern-datatools.com/blog/ai-agents-data-teams-hype-vs-reality</link>
      <guid>https://www.modern-datatools.com/blog/ai-agents-data-teams-hype-vs-reality</guid>
      <pubDate>Wed, 24 Jun 2026 21:16:34 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Every vendor now has an &apos;AI agent.&apos; After evaluating dozens of them, here&apos;s what actually works and what&apos;s still a demo-only fantasy.</description>
    </item>
    <item>
      <title>Three AI Agents Worth Building Once Your Data Stack Doesn’t Suck</title>
      <link>https://www.modern-datatools.com/blog/three-ai-agents-worth-building-once-your-data-stack-doesnt-suck</link>
      <guid>https://www.modern-datatools.com/blog/three-ai-agents-worth-building-once-your-data-stack-doesnt-suck</guid>
      <pubDate>Sat, 06 Jun 2026 18:52:05 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Three AI agents worth building on top of a modern data stack: analytics, incident triage, and internal knowledge assistants.</description>
    </item>
    <item>
      <title>From Modern Data Stack to AI Agents: A Practical Stack for 2026</title>
      <link>https://www.modern-datatools.com/blog/from-modern-data-stack-to-ai-agents-a-practical-stack-for-2026</link>
      <guid>https://www.modern-datatools.com/blog/from-modern-data-stack-to-ai-agents-a-practical-stack-for-2026</guid>
      <pubDate>Mon, 25 May 2026 21:43:33 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>How to add AI on top of a modern data stack: the practical layers, tools, and guardrails for building useful agents, retrieval, and internal copilots.</description>
    </item>
    <item>
      <title>How to Build a Modern Data Stack in 2026: A Practitioner&apos;s Guide</title>
      <link>https://www.modern-datatools.com/blog/modern-data-stack-2026-guide</link>
      <guid>https://www.modern-datatools.com/blog/modern-data-stack-2026-guide</guid>
      <pubDate>Mon, 11 May 2026 20:30:49 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>The modern data stack promised simplicity, but in practice it often feels like IKEA furniture. Here’s how to build one that actually works, layer by layer.</description>
    </item>
    <item>
      <title>We Built an AI Product. The Hard Part Wasn&apos;t the AI.</title>
      <link>https://www.modern-datatools.com/blog/data-is-the-real-ai-moat</link>
      <guid>https://www.modern-datatools.com/blog/data-is-the-real-ai-moat</guid>
      <pubDate>Sun, 03 May 2026 10:40:37 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Everyone&apos;s obsessed with AI models. After building a 500-tool data directory, we learned the model is 10% of the work. The other 90% is data collection, validation, and quality — and that&apos;s the real moat.</description>
    </item>
    <item>
      <title>Best Open-Source Alternatives to Snowflake, Databricks, and Fivetran</title>
      <link>https://www.modern-datatools.com/blog/best-open-source-alternatives-snowflake-databricks-fivetran</link>
      <guid>https://www.modern-datatools.com/blog/best-open-source-alternatives-snowflake-databricks-fivetran</guid>
      <pubDate>Wed, 29 Apr 2026 11:20:58 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Save 84% on your data stack with open-source alternatives. ClickHouse instead of Snowflake, Airbyte instead of Fivetran, Metabase instead of Looker — with real cost comparisons.</description>
    </item>
    <item>
      <title>Keep Your Warehouse from Drowning in Cold Data: A Practical Baselining Playbook</title>
      <link>https://www.modern-datatools.com/blog/data-baselining-warehouse-lifecycle-2026</link>
      <guid>https://www.modern-datatools.com/blog/data-baselining-warehouse-lifecycle-2026</guid>
      <pubDate>Thu, 23 Apr 2026 14:05:25 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>A three-layer playbook — lifecycle policies, usage-based cleanup, and team rituals — to stop warehouse storage from compounding silently.</description>
    </item>
    <item>
      <title>The Real Cost of Snowflake vs Databricks vs BigQuery in 2026</title>
      <link>https://www.modern-datatools.com/blog/real-cost-snowflake-databricks-bigquery-2026</link>
      <guid>https://www.modern-datatools.com/blog/real-cost-snowflake-databricks-bigquery-2026</guid>
      <pubDate>Sat, 18 Apr 2026 00:00:00 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Everybody asks &apos;which data warehouse is cheapest?&apos; but that&apos;s the wrong question. Here&apos;s what actually determines your bill.</description>
    </item>
    <item>
      <title>I Reviewed 500+ Data Tools. Here Are the 10 Things the Best Ones Get Right.</title>
      <link>https://www.modern-datatools.com/blog/500-data-tools-10-things-best-get-right</link>
      <guid>https://www.modern-datatools.com/blog/500-data-tools-10-things-best-get-right</guid>
      <pubDate>Mon, 06 Apr 2026 18:54:04 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>After scoring 500+ data tools on a 100-point framework, clear patterns emerge. Here are the ten that separate great tools from forgettable ones.</description>
    </item>
    <item>
      <title>Benchmarking 5 Local LLMs for Content Generation. Only One Survived.</title>
      <link>https://www.modern-datatools.com/blog/benchmarking-local-llms-qwen-model-comparison</link>
      <guid>https://www.modern-datatools.com/blog/benchmarking-local-llms-qwen-model-comparison</guid>
      <pubDate>Fri, 03 Apr 2026 20:40:37 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Local LLMs are practical for content generation, legal document processing, and internal knowledge bases. I benchmarked five Qwen models on my MacBook Pro. Qwen 3 14B scored 91/100 avg vs 62 for Qwen 2.5 14B -- same size, dramatically better. Newer models performed worse.</description>
    </item>
    <item>
      <title>How I’d Start an AI-Assisted Development Project in 2026</title>
      <link>https://www.modern-datatools.com/blog/how-id-start-an-ai-assisted-development-project-in-2026</link>
      <guid>https://www.modern-datatools.com/blog/how-id-start-an-ai-assisted-development-project-in-2026</guid>
      <pubDate>Sat, 28 Mar 2026 22:38:30 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>A practical 2026 guide to starting an AI-assisted software project — tools, agent orchestration, Git rules, baselining, documentation, and lessons learned.</description>
    </item>
    <item>
      <title>ETL vs ELT in 2026: What&apos;s the Difference and Which Should You Choose?</title>
      <link>https://www.modern-datatools.com/blog/etl-vs-elt-difference-2026</link>
      <guid>https://www.modern-datatools.com/blog/etl-vs-elt-difference-2026</guid>
      <pubDate>Fri, 27 Mar 2026 20:30:49 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>ETL transforms data before loading; ELT loads first and transforms in the warehouse. Learn when to use each approach with real examples, tool comparisons, and a decision framework.</description>
    </item>
    <item>
      <title>The Modern Data Stack in 2026: Complete Guide</title>
      <link>https://www.modern-datatools.com/blog/modern-data-stack-2026-complete-guide</link>
      <guid>https://www.modern-datatools.com/blog/modern-data-stack-2026-complete-guide</guid>
      <pubDate>Sat, 21 Mar 2026 21:36:49 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>A comprehensive guide to every layer of the modern data stack — ingestion, warehousing, transformation, orchestration, BI, data quality, reverse ETL, and streaming — with real tool recommendations and pricing.</description>
    </item>
    <item>
      <title>The Hidden Bugs in Data Pipelines That No One Tests For</title>
      <link>https://www.modern-datatools.com/blog/the-hidden-bugs-in-data-pipelines-that-no-one-tests-for</link>
      <guid>https://www.modern-datatools.com/blog/the-hidden-bugs-in-data-pipelines-that-no-one-tests-for</guid>
      <pubDate>Fri, 20 Mar 2026 14:27:44 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Data pipelines pass all tests but silently lose millions in revenue. Discover Automated Data Tests (ADT)—lightweight checks that catch join drops, sum errors, and aggregation glitches across billions of rows. Python and SQL solutions coming next.</description>
    </item>
    <item>
      <title>How Spec‑Driven Development Powers AI Coding in 2026</title>
      <link>https://www.modern-datatools.com/blog/how-specdriven-development-powers-ai-coding-in-2026</link>
      <guid>https://www.modern-datatools.com/blog/how-specdriven-development-powers-ai-coding-in-2026</guid>
      <pubDate>Wed, 04 Mar 2026 17:10:26 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>How specs make AI coding reliable—and redefine the manager&apos;s role</description>
    </item>
    <item>
      <title>Welcome to the blog!</title>
      <link>https://www.modern-datatools.com/blog/welcome-to-the-blog</link>
      <guid>https://www.modern-datatools.com/blog/welcome-to-the-blog</guid>
      <pubDate>Sat, 28 Feb 2026 20:09:08 GMT</pubDate>
      <author>Egor Burlakov</author>
      <description>Tech front lines to AI era: real stories on leading teams, testing tools, and data engineering wins—short, human-crafted lessons for your daily grind.</description>
    </item>
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