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<div style="display: none; max-height: 0px; overflow: hidden;">The integration of continuous testing within Airflow is essential, as it allows for ongoing verification of data integrity alongside operations. </div>
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<h1><strong>TLDR Data <span id="date">2025-09-25</span></strong></h1>
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<strong>Top CDOs are turning to Maia - a team of agentic data engineers - to scale data delivery (Sponsor)</strong>
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Organizations like Merck, GE Healthcare and Precision for Medicine are using Maia to solve what other AI tools can't: the grinding work that backlogs data teams.<p></p><p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.matillion.com%2Fvideo%2Fmaia-explained-agentic-data-engineering%3Fsrc=web-generated%26utm_medium=email-newsletter%26utm_source=tldr%26utm_campaign=2025-00-web-generated%26utm_content=sept-25-newsletter%26utm_partner=%26utm_term=pp/1/010001998056b37b-4781a78a-3c82-4455-8196-822cbdfca4b5-000000/yzVUBbJ5et1wdMszzbAtRy0jnuEvc5nh_XmlFDfLoPI=424" rel="noopener noreferrer nofollow" target="_blank"><span>Maia deploys agentic data engineers that act as AI teammates</span></a> to build and optimize pipelines, automate repetitive tasks, and deliver trusted data faster.</p>
<p>Whether you're modernizing 15-year-old legacy ETL systems or handling ongoing modeling and analysis, Maia streamlines complex workflows, removing the bottlenecks that hold your data initiatives back.</p>
<p>The result? Your team stops grinding and starts innovating.</p>
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<h1><strong>Deep Dives</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnetflixtechblog.com%2Fscaling-muse-how-netflix-powers-data-driven-creative-insights-at-trillion-row-scale-aa9ad326fd77%3Futm_source=tldrdata/1/010001998056b37b-4781a78a-3c82-4455-8196-822cbdfca4b5-000000/sOyE2TDMIfPDfXhVMvjsis9gtgaGuccu5Z0ID-J4v-o=424">
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<strong>Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale (8 minute read)</strong>
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Muse began as a simple batch pipeline on Druid, but growing demands for advanced filtering, audience-based grouping, and analytics caused a combinatorial data explosion that strained query performance. Netflix re-architected it to handle trillion-row scale data by using HyperLogLog sketches for approximate distinct counts, precomputed in-memory aggregates, and optimized Druid storage/queries, which reduced P99 query latency by 50%.
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<strong>Past Years in Data Engineering and Current Trends (2025 Edition - Part 2) (17 minute read)</strong>
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Modern data stacks are shifting rapidly towards modular SaaS components and AI-powered capabilities, with stack templates accelerating deployment and embedding governance, while ARM and GPU architectures deliver compelling cost and throughput advantages (e.g., AWS Graviton3: 40% improved price/performance, Aerospike: 27% annual cost reduction). Unified query routing and open table formats enable multi-engine collaboration and mitigate vendor lock-in. Innovations in storage target AI/ML workloads and vector search and in-database AI functions are drastically reducing latency (sub-50ms inference), costs (e.g., 35% operational savings), and barriers to advanced analytics, transforming warehouses into active intelligence hubs.
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<strong>What the Fuss with Fluss: Flink Delta Force (9 minute read)</strong>
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Flink 2.1 introduces DeltaJoin and MultiJoin, radically reducing join state bloat by offloading history to external stores like Apache Fluss, transforming streaming joins from terabyte-scale state management to on-demand lookups with minimal checkpoint overhead. DeltaJoin enables elastically scalable enrichment use cases, with cache-managed lookups shaving recovery times from minutes to seconds, but delivers only eventual consistency. For teams managing high-volume, bounded-dimension joins in Flink, this architecture cuts operational pain and state management complexity, though workloads needing snapshot consistency or high-cardinality, high-churn dimensions require alternative engines like RisingWave or Feldera.
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<strong>Streaming and the RAD Stack (8 minute read)</strong>
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Implementing streaming data products with the RAD (Rust, Arrow, and DataFusion) stack enables substantial throughput improvements—often 2x to 5x—thanks to efficient columnar, vectorized execution, as observed in extensive real-world benchmarks. While DataFusion's architecture offers deep extensibility, adapting it for robust streaming use cases requires targeted modifications, especially around checkpointing, sink connectors, and operator emission semantics. Iron Vector, a Rust-based accelerator for Apache Flink SQL/Table API, exemplifies this approach, delivering up to 2x performance gains without code changes.
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<h1><strong>Opinions & Advice</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.astronomer.io%2Fblog%2Forchestrating-data-quality-with-airflow%2F%3Futm_source=tldrdata/1/010001998056b37b-4781a78a-3c82-4455-8196-822cbdfca4b5-000000/2gA77bEoA5c5KxquMe58RXN2IgpvYa0gUgibnjpxYZg=424">
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<strong>Orchestrating Data Quality with Airflow (7 minute read)</strong>
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Maintaining high data quality is challenging due to unclear ownership, bugs, messy source data, and constant changes. The integration of continuous testing within Airflow is essential, as it allows for ongoing verification of data integrity alongside operations. By utilizing reusable task groups, data engineers can embed quality checks directly into their workflows, promoting a proactive approach to maintaining data trust and reliability.
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<strong>Use Cases in Production Make a Data Career (6 minute read)</strong>
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Prioritizing hands-on experience with production-grade data use cases is essential for data professionals seeking rapid career growth, credibility, and leadership opportunities. The focus should be on roles or teams actively deploying, maintaining, and owning data solutions, as deploying POCs is no longer sufficient—ownership and end-to-end involvement drive value and promotions.
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<h1><strong>Launches & Tools</strong></h1>
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Bytewax is a Python-native, stateful stream processing framework with a Rust-based engine designed to build real-time data pipelines and applications. It supports scalable deployments, manages state automatically, and offers a flexible API for operations like map, filter, join, and windowing while integrating seamlessly with Python libraries and data sources like Kafka and filesystems.
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<strong>Apache Airflow 3.1 Release Imminent (3 minute read)</strong>
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Apache Airflow 3.1 introduces significant enhancements, including Human-in-the-Loop integration for manual pipeline interventions, a customizable React plugin system, and various UI improvements that enhance user experience. The update includes internationalization support, making it more accessible for global teams.
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Firecracker, AWS's lightweight virtualization technology, has evolved from its Lambda roots by emphasizing simplicity, strong isolation, and fast startup through snapshotting and cloning. It now supports Bedrock AgentCore and databases (Aurora DSQL) to ensure session and transaction isolation.
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<strong>Postgres' Original Project Goals: The Creators Totally Nailed It (9 minute read)</strong>
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Postgres was designed in the 1980s with goals like handling complex data, being extensible, supporting triggers, simplifying recovery, using new hardware, and staying true to the relational model. Decades later, PostgreSQL has achieved all of these, with features like JSONB, PostGIS, triggers, robust crash recovery, and strong performance on modern hardware.
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<strong>Cloudflare's 2025 Annual Founders' Letter (9 minute read)</strong>
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There has been a seismic shift from traditional Search Engines to AI-powered Answer Engines. Direct answers are replacing web traffic as the Internet's core value exchange. This change is causing drastic declines in traffic (especially for media and research organizations). It threatens the sustainability of content-driven business models. A new ecosystem is emerging where compensation flows to creators of original, uniquely valuable data, with AI companies expected to financially support the content fueling their models.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhackernoon.com%2Fresearch-round-up-on-anonymization-creating-data-that-enables-generalization-without-memorization%3Futm_source=tldrdata/1/010001998056b37b-4781a78a-3c82-4455-8196-822cbdfca4b5-000000/DaDCPJCzXmCpQtt2D7TTBOVFKhRhHdqNv3qEHMIC8LY=424">
<span>
<strong>On Anonymization: Creating Data That Enables Generalization Without Memorization (4 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Anonymization, rather than mere privacy compliance, is emerging as the key enabler for unlocking sensitive data for safe, responsible AI and analytics. Techniques like Microsoft's Private Evolution (PE), Google's VaultGemma, and Stained Glass Transformations (SGT) enable synthetic data generation and secure inference without revealing individual records, showing how stronger anonymization improves both generalization and model utility. Enterprise adoption by Apple, Microsoft, and Google signals a shift toward models capable of provably constraining memorization and supporting data-driven innovation under robust anonymization guarantees.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdtyped.com%2Fdemystifying-the-medallion-architecture%2F%3Futm_source=tldrdata/1/010001998056b37b-4781a78a-3c82-4455-8196-822cbdfca4b5-000000/DpqUamzZdcon-bDai7JXQX3xWk_a35_EVa8aUAHIIPc=424">
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<strong>Demystifying the Medallion Architecture (8 minute read)</strong>
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The Medallion Architecture is a Databricks design pattern that organizes data into Bronze (raw), Silver (cleaned), and Gold (business-ready) layers.
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<strong>Select Star Joins Snowflake and Other Industry Leaders to Launch Open Semantic Interchange: Turn BI Dashboards into AI-Ready Semantic Models (7 minute read)</strong>
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The OSI partnership means data teams can get consistent, portable metrics across BI and AI tools, reducing migration costs and enabling AI-ready analytics.
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