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<h1><strong>TLDR Data <span id="date">2026-06-18</span></strong></h1>
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<h1><strong>Deep Dives</strong></h1>
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OpenAI's Deployment Simulation is a new pre-release safety technique that replays real (de-identified) user conversation prefixes from previous deployments and generates responses with a candidate model to predict how it will actually behave in production, providing far more realistic coverage and better rate estimates of undesired behaviors than traditional evaluations.
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Rippling rebuilt its go-to-market data foundation into an AI Database on a lakehouse to power agentic operations, replacing fragmented warehouses and manual processes with a unified intelligence layer. Key elements include ML-based entity resolution across third-party sources, medallion architecture, semantic search over enriched conversation data, and a natural-language Genie interface used by AI agents.
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Instacart rebuilt its ads retrieval system by moving from a scoring-based BERT model (that scored every product ID) to a generative approach that “spells out” recommendations token-by-token using Instacart Semantic IDs. This shift solved vocabulary bottlenecks, cold-start problems, and structural drift, enabling better generalization, full catalog coverage, and stronger session understanding.
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Guidewire used Apache Trino to federate SQL across Iceberg, Redshift, OpenSearch, and customer S3 buckets, speeding exploratory ML workflows. Domain catalogs and Lake Formation ABAC plus Trino RBAC handled isolation, but Trino's S3 layer exposed a governance gap.
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<h1><strong>Opinions & Advice</strong></h1>
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<strong>Why We Moved from Hive-Style Data Lakes to Apache Iceberg? (12 minute read)</strong>
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Moving from a Hive-style partitioned data lake (Parquet files on S3 + AWS Glue Catalog) to Apache Iceberg after AWS strengthened its ecosystem support (including S3 Tables) solved long-standing pain points with Hive-style architectures. Iceberg enables efficient pruning, seamless schema and partition evolution, time travel, and better query planning performance.
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<strong>Data Processing is Becoming a GPU Workload (6 minute read)</strong>
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Data processing is moving from CPU-centric SQL ETL to GPU-heavy inference pipelines for video, audio, PDFs, Slack, and sensor data. Models now curate data first, creating embeddings, labels, summaries, and structured records for SQL and vector systems, which pushes platforms toward mixed compute, streaming, and API-aware concurrency.
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<strong>The Identity Crisis: Why Entity Resolution Is the Missing Foundation of Every Data Product Stack (10 minute read)</strong>
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Identity resolution and warehouse-native MDM are core infrastructure for trusted data products, AI, and compliance. At enterprise scale, local checks fall short, creating duplicate customers, phantom entities, and model-poisoning risk. The pattern combines blocking, rule-based and ML matching, graph clustering, and human review inside the warehouse.
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<h1><strong>Launches & Tools</strong></h1>
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<strong>Insights from the 2026 State of Analytics Engineering (Sponsor)</strong>
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AI is reshaping analytics engineering, but organizations continue to face challenges around trust, governance, and cost management. Drawing on insights from hundreds of analytics and data professionals, this report examines the trends, priorities, and practices defining the next generation of data teams. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ffandf.co%2F4vGuPvZ/1/0100019eda33ff6a-d0d5ce4f-c777-45d5-9083-fb434bd3b3a5-000000/3yaee53dJ1ifLpA0InhEN9Ja9mOrkig29I331SC0ZgQ=452" rel="noopener noreferrer nofollow" target="_blank"><span>Read the dbt Labs report</span></a>
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<strong>Introducing Lakehouse//RT: Real-Time Performance on a Unified Lakehouse (13 minute read)</strong>
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Databricks Lakehouse//RT is a real-time data warehouse powered by Reyden that delivers millisecond query performance directly on lakehouse data without separate serving layers or data movement. It aims to simplify real-time analytics, BI, app serving, and observability by keeping performance, governance, and open data formats inside the Databricks Lakehouse.
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datapitfalls is an open-source Claude-powered tool that audits charts, code, written analysis, and documents for common data mistakes across the full reasoning chain, from question framing to communication. It goes beyond chart linting by using Ben Jones' Avoiding Data Pitfalls taxonomy to identify issues like bias, silent pipeline failures, bad aggregation, statistical errors, misleading visuals, and unclear presentation, then explains severity and fixes.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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Salesforce's Data 360 segmentation processes 1 quadrillion records per month across arbitrary customer schemas, relationship graphs, and storage systems, while running about 3 million Spark jobs monthly. Metadata became the bottleneck, with some environments hitting 3,000 to 6,000 tables, 500+ MB metadata payloads, and billions of candidate query plans.
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Heidi AI fine-tuned a smaller clinical model to match a frontier model in blinded clinician preference tests. Its edge comes from proprietary clinician feedback, safety checks, and a product loop tuned to real clinical judgment.
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<strong>The NULL in your NOT IN (13 minute read)</strong>
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NOT IN in PostgreSQL is a classic gotcha: a single NULL in the subquery (or left side) causes the entire query to return zero rows due to three-valued logic.
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<strong>Probably (Tool)</strong>
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Probably is a local data agent for natural-language analysis across files and warehouses.
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