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<div style="display: none; max-height: 0px; overflow: hidden;">Relationship-Based Access Control (ReBAC) models data access as a graph of relationships between users and resources, providing flexibility </div>
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<h1><strong>TLDR Data <span id="date">2026-01-12</span></strong></h1>
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<strong>RAG with Access Control (7 minute read)</strong>
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Relationship-Based Access Control (ReBAC) models data access as a graph of relationships between users and resources, providing flexibility for dynamic and context-rich applications. SpiceDB, an open-source Zanzibar-based ReBAC implementation, can be integrated with vector databases like Pinecone to secure RAG pipelines with fine-grained policies. OpenAI uses SpiceDB to enforce access policies over 37 billion documents across 5 million ChatGPT Connector users, effectively preventing information leakage when serving domain-specific knowledge.
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<strong>The Journey to Zero-Copy: How chDB Became the Fastest SQL Engine on Pandas DataFrame (11 minute read)</strong>
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chDB is a Python library that embeds ClickHouse so you can run high-performance SQL directly on Pandas DataFrames without serialization overhead. It achieves true zero-copy input by automatically discovering DataFrames and wrapping their memory directly. Critical components were rewritten in C++ to avoid Python performance limits like the GIL and string encoding overhead. In v4.0, it completed the loop with zero-copy output using direct NumPy type mapping and shared memory buffers.
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<strong>Apache Hudi 1.1 Deep Dive: Async Instant Time Generation for Flink Writers (11 minute read)</strong>
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Apache Hudi 1.1 introduces asynchronous instant time generation for Flink writers, allowing them to request and receive a new instant time before the previous instant is fully committed, eliminating blocking delays that previously caused throughput fluctuations, backpressure, and ingestion instability during the gap between data flushing and checkpoint completion.
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<strong>A Critique of Iceberg REST Catalog: A Classic Case of Why Semantic Spec Fails (5 minute read)</strong>
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Apache Iceberg's REST Catalog specification ensures semantic interoperability, enabling diverse engines such as Trino, Spark, and Flink to interact seamlessly via a universal API. However, the standard omits operational guarantees (no defined latency, throughput, or synchronization SLAs), leading to unpredictable performance, high retry amplification, and systemic instability as table and catalog counts scale. This lack of operational constraints shifts the burden to clients and operators, making systems fragile and hard to maintain, highlighting the need for explicit behavioral contracts and conformance testing to ensure reliability at enterprise scale.
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<strong>Beyond One-Size-Fits-All RAG: Why Different Knowledge Sources Need Different Retrieval Strategies (12 minute read)</strong>
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Stop treating RAG as one vector-search pipeline: different knowledge sources demand different retrieval contracts, or your quality/latency/cost will implode in production. Use source-specific strategies: contextualized chunk embeddings for long docs, doc-level summaries + reranking for content discovery, and hybrid keyword+semantic retrieval with rule-type adjudication for compliance. The trade-off is more ingest logic and ops complexity, but you win by batching enrichment, caching judgments, and validating/grounding outputs so they can't cite beyond retrieved evidence.
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<strong>LLM Predictions for 2026, Shared with Oxide and Friends (5 minute read)</strong>
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2026 will mark the year when LLM-generated code quality becomes undeniably excellent, effective sandboxing solutions finally emerge to safely run untrusted code, and a major security incident ("Challenger disaster") exposes risks from over-privileged coding agents. Looking further ahead, the Jevons paradox for software engineering will resolve within 3 years.
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Small, accumulating failures across the clickstream data lifecycle, such as instrumentation drift, unvalidated developer changes, bot traffic inflation, ad-blocker suppression, pipeline downtime, and misconfigurations, can create significant discrepancies (e.g., 18% fewer sign-ups in analytics vs. backend database) that erode overall data trust, leading to unreliable analytics, delayed decisions, and risks for AI models.
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<strong>Spark Declarative Pipelines Programming Guide (6 minute read)</strong>
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Introducing MCP CLI: A way to call MCP Servers Efficiently (7 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
MCP-CLI is a lightweight, open-source command-line tool that enables efficient, dynamic interaction with Model Context Protocol (MCP) servers. By supporting just-in-time tool discovery and execution instead of statically loading all tool definitions, it drastically reduces token consumption (up to 99% savings), making it ideal for AI coding agents like Gemini CLI or Claude Code.
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<strong>Databricks x Palantir | Partnership Deep Dive (16 minute video)</strong>
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Databricks and Palantir are building a two-way integration so teams can access the same data from either platform without copying it. The integration centers on data federation, governance, compute pushdown, and workflow and model sharing.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.infoworld.com%2Farticle%2F4114910%2Fsnowflake-to-acquire-observe-to-boost-observability-in-aiops.html%3Futm_source=tldrdata/1/0100019bb1e3c51b-d59bab1c-3031-4694-b9dc-8ef92ac3f87a-000000/rFc0nmhVMDi3-NFQQTgw5teFhWRQerYeuxG3_93-oV0=439">
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<strong>Snowflake to acquire Observe to boost observability in AIops (3 minute read)</strong>
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Snowflake's planned acquisition of Observe aims to consolidate observability, telemetry, and model monitoring within its Data Cloud.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftowardsdatascience.com%2Ffaster-is-not-always-better-choosing-the-right-postgresql-insert-strategy-in-python-benchmarks%2F%3Futm_source=tldrdata/1/0100019bb1e3c51b-d59bab1c-3031-4694-b9dc-8ef92ac3f87a-000000/UuwUySiqUqsXeq_2P7f8z-nBCzrBL0VKnl48oWD6AUM=439">
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<strong>Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks) (4 minute read)</strong>
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Benchmarks show psycopg3 bulk inserts can top 2M rows/sec, but driver choice should balance throughput with abstraction and safety.
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