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<div style="display: none; max-height: 0px; overflow: hidden;">Distributed pipelines that curate petabyte-scale image datasets enabled AI researchers to deduplicate, filter, and cluster billions of web images </div>
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<h1><strong>TLDR Data <span id="date">2025-10-30</span></strong></h1>
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Datology built distributed pipelines to curate petabyte-scale image datasets, enabling AI researchers to deduplicate, filter, and cluster billions of web images using custom Spark/Ray operations. Powered by a modified Flyte orchestrator and Postgres catalog, it deploys seamlessly into customer environments, delivering faster, cheaper, and more efficient models.
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Resource Description Framework reliance on global Internationalized Resource Identifiers (IRI) is limiting for real-world data modeling, where context and local scoping often define semantics more effectively than strict, global standards. Knowledge graphs benefit from contextual, composable property shapes (via SHACL), late binding, and scoped ontologies. This enables schema evolution, effective disambiguation, and federated interoperability without global identifier consensus (via blank nodes). Applying these innovations enables continuous integration of new facts, entity resolution, and knowledge refinement into ever-evolving world views.
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Apache Iceberg's immutable snapshots excel at batch processing, but struggle with real-time CDC: frequent small updates rely on costly equality deletes. Iceberg v3 introduces deletion vectors for precise row masking without full scans, and row lineage for stable identities, enabling efficient CDC views. v4 proposes a single Root Manifest per snapshot to consolidate deltas, allowing CDC readers to diff changes with minimal I/O.
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<strong>Valkey 9.0 Debuts Multidatabase Clustering for Massive-Scale Workloads (3 minute read)</strong>
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Valkey is an in-memory datastore, backward-compatible with Redis, under the BSD 3-Clause License. Valkey 9.0 introduces multidatabase clustering, atomic slot migration, and major performance optimizations, virtually scaling over 1 billion requests per second. This aims to ease migration for large-scale, production-critical workloads across cloud and on-premises environments.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.dremio.com%2Fblog%2Fwhats-new-in-apache-polaris-1-2-0-fine-grained-access-event-persistence-and-better-federation%2F%3Futm_source=tldrdata/1/0100019a3495379f-8f1f93a0-3251-4dea-8990-4e0bba1cb794-000000/JjKPPLOHiVPCmiLq5Kq_3RhAJi45fT5JubF6rIFUsK4=429">
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<strong>What's New in Apache Polaris 1.2.0: Fine-Grained Access, Event Persistence, and Better Federation (4 minute read)</strong>
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Apache Polaris 1.2.0 introduces granular access controls, sub-catalog RBAC for federated catalogs, and persistent catalog event logging (currently in preview), enhancing governance and observability across multi-engine Iceberg lakehouses. Additional features include IAM-based authentication for Amazon RDS/Aurora PostgreSQL, extended S3-compatible storage support, and streamlined credential management. The release strengthens security, catalog integrity, and operational flexibility.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Backpressure in Distributed Systems (20 minute read)</strong>
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Backpressure happens when fast producers overwhelm slower consumers, causing memory issues, dropped data, or high latency. Systems handle it by slowing producers, dropping queued or incoming messages, or scaling consumers so processing keeps pace. The key insight for data professionals is to design pipelines with explicit backpressure strategies rather than relying on infinite buffering, which helps maintain stability and predictable performance in distributed systems.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhuggingface.co%2Fblog%2Fhuggingface-hub-v1%3Futm_source=tldrdata/1/0100019a3495379f-8f1f93a0-3251-4dea-8990-4e0bba1cb794-000000/cYLc1-B1_YeHIFBcIHCmm9VcjzA7Nz-Wg0a84xyVeLw=429">
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<strong>huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning (8 minute read)</strong>
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huggingface_hub has reached v1.0 after five years. It now serves as a core dependency for 200,000+ repositories and powers access to over 2 million models, 500,000 datasets, and 1 million Spaces. Version 1.0 delivers major upgrades: a migration to httpx (enabling HTTP/2 and unified async/sync APIs), hf_xet for chunk-based file transfers (77 PB migrated), and a fully revamped CLI with Typer.
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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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<strong>Streaming Datasets: 100x More Efficient (4 minute read)</strong>
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Hugging Face's new `streaming=true` enables instant high-speed training on multi-terabyte remote datasets, often faster than local SSDs.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fthenewstack.io%2Fopentelemetry-adoption-update-rust-prometheus-and-other-speed-bumps%2F%3Futm_source=tldrdata/1/0100019a3495379f-8f1f93a0-3251-4dea-8990-4e0bba1cb794-000000/ojUcn0ikGAWSw2Zw5j3_eZ2nLAg574MhAxq50DZOxes=429">
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<strong>OpenTelemetry Adoption Update: Rust, Prometheus and Other Speed Bumps (5 minute read)</strong>
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OpenTelemetry is becoming the standard for observability, but adoption is slowed by complexity and incomplete language support, especially for Rust.
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