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<div style="display: none; max-height: 0px; overflow: hidden;">Context graphs help AI understand how work actually gets done by capturing real processes, relationships, and activity across enterprise tools </div>
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<h1><strong>TLDR Data <span id="date">2026-01-08</span></strong></h1>
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<p>>> <strong>Reach 6M monthly active shoppers.</strong> </p>
<p>>> <strong>Build trust</strong> with the power of Microsoft (with potential co-selling further down the road). </p>
<p>>> <strong>Support flexible go-to-market models</strong>: self-serve, sales-led, and channel-driven. </p>
<p>>> <strong>Extend your reach</strong> with a network of 500K partners. </p>
<p>>> <strong>See real results:</strong> <a class="Hyperlink SCXW60340355 BCX0" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.microsoft.com%2Fen-us%2Fsoftware-development-companies%2Fgrow-sales%2Fmarketplace%3Futm_source=fnf%26utm_medium=newsletter%26utm_campaign=microsoft-jan%26utm_term=tldrdata%26utm_content=marketplace%23case-study-carousel-3/1/0100019b9d4a804e-ddc72d05-082e-4d17-b941-e976aee08872-000000/r1hE95H5DnIV6sFc5IuKfGHm8SdVWXaEe0nBhjnHVPo=439" rel="noreferrer noopener" target="_blank"><span>Neo4j</span></a> 6x-ed revenue; <a class="Hyperlink SCXW60340355 BCX0" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.microsoft.com%2Fen%2Fcustomers%2Fstory%2F24802-pangaea-data-microsoft-commercial-marketplace/1/0100019b9d4a804e-ddc72d05-082e-4d17-b941-e976aee08872-000000/hunPBqML-s0lcuQEtzzUw-E5X6cUx1HuSbge-wbP-aI=439" rel="noreferrer noopener" target="_blank"><span>Pangaea Data</span></a> cut 78% of onboarding time; <a class="Hyperlink SCXW60340355 BCX0" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.microsoft.com%2Fen%2Fcustomers%2Fstory%2F25531-contentsquare-azure-synapse-analytics/1/0100019b9d4a804e-ddc72d05-082e-4d17-b941-e976aee08872-000000/eSiYomnT5UYv2hXTP6LRod2jxDqDYSMxOAJ4jwZWJaQ=439" rel="noreferrer noopener" target="_blank"><span>Contentsquare</span></a> closes 106% higher ACV deals. </p>
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<strong>Migration at Scale: Moving Marketing Cloud Caching from Memcached to Redis at 1.5M RPS Without Downtime (6 minute read)</strong>
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Salesforce's Marketing Cloud team executed a zero-downtime migration from Memcached to Redis Cluster, supporting 1.5 million cache events per second for over 50 services under live production traffic. The transition leveraged a Dynamic Cache Router, real-time routing, compatibility layers for TTL and key handling, and hot-key detection using a Count-Min Sketch model. End-to-end cache latency was consistently maintained at P50 ≈ 1 ms and P99 ≈ 20 ms, ensuring operational continuity.
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Semantic layers should be simple, shared, and independent of any single BI tool so the same definitions work for dashboards, APIs, and AI. Using dlt for schema discovery, an LLM to draft the model, and a “boring” semantic layer, most of the semantic work is automated while keeping queries deterministic and debuggable. This enables reliable Chat-BI and multi-modal analytics from one source of truth without heavy infrastructure.
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<strong>Databases in 2025: A Year in Review (8 minute read)</strong>
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In 2025, PostgreSQL dominated with version 18 enhancements, major acquisitions (Databricks-Neon $1B and Snowflake-CrunchyData $250M), emerging sharding projects, and deeper cloud integrations. Trends included widespread Model Context Protocol (MCP) for LLM integration, new columnar formats, DBMS failures, and waning GPU hype.
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<strong>How Multimodal Vector Databases Are Transforming Challenges Across Industries (12 minute read)</strong>
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Multimodal vector databases like ApertureDB natively handle diverse data types in a unified system, offering semantic search, graph-based relations, and full traceability for advanced cross-modal retrieval and complex queries. They tackle healthcare compliance challenges by automating the detection of regulatory omissions in drug advertisements, leveraging tools such as Unstructured.io, GPT-4o, and CLIP embeddings.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Mastering LLM Tool Calling: The Complete Framework for Connecting Models to the Real World (6 minute read)</strong>
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LLM tool calling is the mechanism enabling models to invoke external functions/APIs, turning static LLMs into dynamic agents for data access, computation, and real-world actions. It includes three pillars to build reliable, production-ready agents: Data Access (retrieval tools), Computation (precision processing), and Actions (effecting changes).
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<strong>How AI Transformed Database Debugging at Databricks (8 minute read)</strong>
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Databricks developed an internal AI-powered agentic platform that transforms database debugging for thousands of OLTP instances by enabling conversational, natural-language interactions with unified access to metrics, logs, and expert knowledge. This evolution from rigid checklists and anomaly detection to an interactive multi-agent system has reduced debugging time by up to 90%, allowing engineers to resolve complex issues more efficiently.
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<h1><strong>Quick Links</strong></h1>
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<strong>The Adaptive Retrieval Advantage: Why Query-Aware RAG Is Replacing One-Size-Fits-All Retrieval (10 minute read)</strong>
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Lightweight query classification slashes RAG costs and latency while boosting precision for complex enterprise queries.
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<strong>Trending: What Context Graphs Mean For Enterprise (8 minute read)</strong>
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Context graphs capture how work really happens, including the hidden workflows and coordination that drive outcomes.
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