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<div style="display: none; max-height: 0px; overflow: hidden;">Pinterest transfers hundreds of terabytes daily from numerous sharded MySQL sources to analytical systems using Kafka Connect and Debezium. </div>
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<h1><strong>TLDR Data <span id="date">2025-10-23</span></strong></h1>
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Learn how 70+ companies improved performance, scaled globally, and optimized costs using Google Cloud's managed database services: <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcloud.google.com%2Fresources%2Fcontent%2Fdatabases-customer-stories-2025%3Fe=48754805%26hl=en%26utm_source=cloud_sfdc%26utm_medium=email%26utm_campaign=FY25-Q2-GLOBAL-ENT35086-website-dl-DBCustStories25-62347%26utm_content=tldr%26utm_term=oct_23/2/0100019a1088f24e-957a2659-fe4d-4f80-bfed-17033216f9f2-000000/W40dYztBE2WDtE1LHJg9FJSz4NB_wvKlYVYZo4zJrwI=428" rel="noopener noreferrer nofollow" target="_blank"><span>AlloyDB, Cloud SQL, Spanner, Memorystore, Bigtable, and Firestore</span></a>. Each case study is a one-pager that distills the key insights from deployments at companies like Macy's, Wayfair, Yahoo, and many others. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcloud.google.com%2Fresources%2Fcontent%2Fdatabases-customer-stories-2025%3Fe=48754805%26hl=en%26utm_source=cloud_sfdc%26utm_medium=email%26utm_campaign=FY25-Q2-GLOBAL-ENT35086-website-dl-DBCustStories25-62347%26utm_content=tldr%26utm_term=oct_23/3/0100019a1088f24e-957a2659-fe4d-4f80-bfed-17033216f9f2-000000/GK8ydGOvuVq0ayZmJF389r9UPIbcOEntV0ZlUDz-0Vk=428" rel="noopener noreferrer nofollow" target="_blank"><span>Get the resource</span></a>
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<strong>DuckDB Tera Extension (Tool)</strong>
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Query.Farm's Tera extension adds template rendering directly into DuckDB, allowing SQL queries to dynamically generate text, HTML, JSON, or configuration files using the Tera templating engine. It lets you embed variables, loops, and conditions inside templates to produce formatted reports, API responses, or configuration outputs directly from database data without leaving SQL.
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<strong>IndexTables for Spark (GitHub Repo)</strong>
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Built by the IndexTables project, this Apache-2.0-licensed library adds a high-performance, full-text-search-capable open table format integrated with Spark SQL. It enables SQL queries with full text search and fast retrieval across large-scale data sets. Still experimental and less mature than mainstream formats, it may offer value when search-style queries dominate and Hadoop/Spark is already in use.
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<strong>Databases Without an OS? Meet QuinineHM (11 minute read)</strong>
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QuinineHM is a “Hardware Manager” that replaces the operating system to run databases directly on bare metal. This removes context-switch and scheduler overhead, exposing CPUs, memory, and NICs directly to workloads for deterministic speed and near-zero attack surface. Its first product, TonicDB, a Redis-compatible in-memory DB, runs up to 20x faster and 3x cheaper.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Identify User Journeys at Pinterest (8 minute read)</strong>
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By modeling user journeys as hierarchical clusters of activities (searches, Pins, boards), Pinterest shifts from short-term interests to personalized, intent-driven recommendations, addressing limited training data for new journey-focused products, with lean, foundation-model-based techniques. The pipeline includes extracting keywords from activities, clustering/embedding them, ranking/naming/expanding journeys, predict stages (situational/evergreen), and output scored lists.
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<strong>Apache Flink Watermarks…WTF? (Website)</strong>
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This interactive website visually illustrates how Flink uses watermarks to manage event-time in streams and establish when it's safe to treat earlier timestamps as complete and trigger windows. Key takeaways: generate timestamps early, use a strategy tailored to your data's out-of-order characteristics, and remember that in multi-input operators the watermark advances only as fast as the slowest upstream source, so skew or idle partitions can bottleneck your pipeline.
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<div style="text-align: center;"><span style="font-size: 36px;">⚡</span></div></div>
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<h1><strong>Quick Links</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.tigerdata.com%2Fblog%2Fpostgres-for-agents%3Futm_source=tldrdata/1/0100019a1088f24e-957a2659-fe4d-4f80-bfed-17033216f9f2-000000/V1G2tBgDSl3SOM2tGqOM9f-fgXtyFNkT6M7JcFPBKGY=428">
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<strong>Postgres for Agents (5 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Agentic Postgres is a version of PostgreSQL built for AI agents.
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<strong>Modernising Grab's Model Serving Platform with NVIDIA Triton Inference Server (6 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Migrating to Triton cut p90 latency 6x and reduced infrastructure costs by ~20% across half of Grab's online ML deployments.
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