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<h1><strong>TLDR Data <span id="date">2026-02-23</span></strong></h1>
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<strong>AI isn't a threat… if you know how to use it (Sponsor)</strong>
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You don't have to panic about AI, but you do need to be prepared. <p></p><p>According to <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgeneralassemb.ly%2Fstudents%2Fcourses%2Fai-data-analytics-pathway/3/0100019c8a2fe32c-1291cfb2-20a9-4333-83e5-cb54faf8cfce-000000/2bsEZgSwGioUCFs2iMcQw7_CA1CUKeNWNp6Z01-Y2sY=445" rel="noopener noreferrer nofollow" target="_blank"><span>General Assembly</span></a>, 75% of businesses have invested in AI analytics. Data workers who adapt will become indispensable in steering AI. The ones who stick to the old way might be out of a job…</p>
<p>📖 Want to get AI-native, fast? General Assembly is now offering <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgeneralassemb.ly%2Fstudents%2Fcourses%2Fai-data-analytics-pathway/4/0100019c8a2fe32c-1291cfb2-20a9-4333-83e5-cb54faf8cfce-000000/BK61XuO1y3uoJcnX21HNEZLQG65X6yOFNgmm_w8wl7I=445" rel="noopener noreferrer nofollow" target="_blank"><span>4 courses for the price of 2</span></a>. Each course is led by a human expert, and provides hands-on practice you can immediately apply to advance your career.</p>
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<h1><strong>Deep Dives</strong></h1>
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<strong>Osprey: Open Sourcing Our Rule Engine (10 minute read)</strong>
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Discord has open-sourced Osprey, a high-performance safety rules engine built to detect and block spam, abuse, bots, scams, and other threats in real time. Osprey processes massive event volumes against dynamic, Python-like SML rules, extracts features, applies labels and effects, and issues fast verdicts or async outputs using a modular design with gRPC/Kafka inputs, pluggable sinks, load-balanced workers, and an intuitive investigation UI.
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<strong>7 Minutes to Understand the New Spark Streaming Feature that Changes Everything (7 minute read)</strong>
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Apache Spark 4.1 introduces Structured Streaming Real-Time Mode (RTM), competing with Flink on millisecond-level latency via concurrent stage scheduling and in-memory streaming shuffle. RTM reduces coordination, planning, and checkpointing overhead, allowing existing Spark users to achieve ultra-low latency by simply switching to the RealTimeTrigger setting without migrating platforms. This extends Spark's capabilities for real-time workloads, maximizing the value of prior Spark investments.
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<strong>Uber's Rate Limiting System (7 minute read)</strong>
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Uber evolved from fragmented, Redis-heavy rate-limiting implementations to a unified Global Rate Limiter (GRL) integrated into the service mesh, handling ~80 million RPCs per second across 1,100+ services. GRL uses a three-tier architecture (data-plane clients for local probabilistic dropping, zone aggregators for metrics, and regional/global controllers for dynamic decisions) combined with a drop-by-ratio soft-limiting approach instead of hard token buckets.
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<strong>Agentic AI systems don't fail suddenly — they drift over time (8 minute read)</strong>
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Agentic AI systems can drift in production, and one-off evaluations rarely catch it. This can quietly reduce key steps, such as verification checks dropping 20 to 30 percent, increasing risk before failures appear. Effective control requires continuous monitoring, clear baselines, and statistical drift detection over time.
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<strong>Dashboards aren't dead, they're just demoted (8 minute read)</strong>
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Dashboards aren't dead, but they've been overused and promoted beyond what they're good at. They work best for consistent, governed reporting and tracking goals, while AI-powered analytics tools are better for answering ad hoc questions and enabling deeper exploration.
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<strong>Ten Years Late to the dbt Party (18 minute read)</strong>
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dbt separates sources, staging, and marts while providing Jinja macros for API ingestion, incremental models for efficiency, snapshots for slowly changing dimensions, freshness checks, testing, and auto-generated lineage documentation.
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<strong>The Enterprise Architecture Of The Future Will Be Heterogenous & Multi-Agent (7 minute read)</strong>
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Future enterprise architecture will be a multi-agent system unifying data and AI in a four-layer stack: a Data Layer for AI-ready context, a Semantic Layer for efficient use, an Agent Build Layer for diverse tools and workflows, and a Trust Layer for end-to-end observability and reliability.
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<strong>Apache Kafka 4.2.0 Release Announcement (11 minute read)</strong>
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Apache Kafka 4.2 makes Kafka Queues production-ready with per-record acknowledgements, stronger long-running processing controls, and improved lag metrics. Kafka Streams server-side rebalancing is now GA, adding DLQ support and more predictable scheduling. It also standardizes CLI flags, fixes metrics, adds idle ratio metrics, and supports Java 25, improving reliability and observability.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fsoda-inria%2Ftabicl%3Futm_source=tldrdata/1/0100019c8a2fe32c-1291cfb2-20a9-4333-83e5-cb54faf8cfce-000000/FeHilMY1KtV9hYK8R3yNHcxf7etwE-BSmXc7piKqxYU=445">
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<strong>TabICLv2: A state-of-the-art tabular foundation model (GitHub Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
TabICLv2 is an open-source, scikit-learn-compatible tabular foundation model for classification and regression that claims state-of-the-art results without hyperparameter tuning, beating tuned XGBoost, CatBoost, and LightGBM on about 80 percent of TabArena datasets. It runs fit and predict in one transformer pass, supports KV caching, and handles up to 100k rows and 2k features.
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<strong>Zvec (GitHub Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Zvec is an open-source, in-process vector database from Alibaba that embeds directly into applications to deliver fast, production-grade similarity search without needing a separate server. It supports dense and sparse vectors, hybrid search, and runs across Python and Node.js on Linux and macOS.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Apache Polaris Graduates to a Top-Level Apache Project (3 minute read)</strong>
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Apache Polaris is now a Top-Level Project at the ASF, marking vendor-neutral governance and strong community backing with 100+ contributors across eight organizations. In 18 months, it delivered six releases, closed 2,800+ pull requests, and standardized the Apache Iceberg REST Catalog across engines like Dremio, Spark, Flink, and Trino.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fblog%2Fengineering%2Fai%2Fscaling-llm-based-ranking-systems-with-sglang-at-linkedin%3Futm_source=tldrdata/1/0100019c8a2fe32c-1291cfb2-20a9-4333-83e5-cb54faf8cfce-000000/fHWnk6VC8yo6QLAgwr0-CM6s0Sv6-2XDbHtC14GtHhU=445">
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<strong>Scaling LLM-Based Ranking Systems with SGLang at LinkedIn (12 minute read)</strong>
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LinkedIn scaled LLM-based ranking for AI People and Job Search by adapting and upstreaming major optimizations to the open-source SGLang LLM serving framework. Its key contributions include in-request batch tokenization, async dynamic batching, scoring-only execution paths, in-batch prefix caching, and multi-process architecture to bypass Python bottlenecks.
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<h1><strong>Quick Links</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.bytebytego.com%2Fp%2Fep203-rabbitmq-vs-kafka-vs-pulsar%3Futm_source=tldrdata/1/0100019c8a2fe32c-1291cfb2-20a9-4333-83e5-cb54faf8cfce-000000/hpT8JEreXA97T9iihnNwPlHxUHujA5tJ8g7A8BaFpuw=445">
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<strong>RabbitMQ vs Kafka vs Pulsar (6 minute read)</strong>
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While all handle messaging, they address fundamentally different problems.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcloud.google.com%2Fblog%2Ftopics%2Fdevelopers-practitioners%2Fusing-the-neo4j-extension-in-gemini-cli%2F%3Futm_source=tldrdata/1/0100019c8a2fe32c-1291cfb2-20a9-4333-83e5-cb54faf8cfce-000000/bFC1FZHOxDvdKaKf0eZJTaILSkeHwBTWsdym8zb7HD0=445">
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<strong>Using the Neo4j Extension in Gemini CLI (5 minute read)</strong>
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The Neo4j Gemini CLI extension integrates Gemini's advanced reasoning directly with Neo4j on Google Cloud, streamlining graph data management and AI-driven knowledge retrieval from the terminal.
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