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<div style="display: none; max-height: 0px; overflow: hidden;">ClickHouse achieved a peak compression of 178x on a 20 GB dataset of 66.75 million entries on Nginx access logs by parsing unstructured logs </div>
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<h1><strong>TLDR Data <span id="date">2025-10-27</span></strong></h1>
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<h1><strong>Deep Dives</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fblog%2Fengineering%2Fai%2Fhow-we-engineered-linkedins-hiring-assistant%3Futm_source=tldrdata/1/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/bdbCyk_efGqV24ZKgS9o41ognG7Hes9SK2kO8Mjcd5Q=428">
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<strong>Building the Agentic Future of Recruiting: How we Engineered LinkedIn's Hiring Assistant (25 minute read)</strong>
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LinkedIn's Hiring Assistant demonstrates how separating planning from execution improves reliability and scalability in AI systems. Continuous learning loops and personalized cognitive memory enable adaptive, context-aware behavior. The design emphasizes trust through human oversight, transparent reasoning, and responsible automation.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fblog%2Flog-compression-170x%3Futm_source=tldrdata/1/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/37KZ3lUhbByXFBmR2y6TcPp5s6HBDnlonqQ87sxZLmA=428">
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<strong>Achieving 170x Compression for Logs (18 minute read)</strong>
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ClickHouse achieved a peak compression of 178x on a 20 GB dataset of 66.75 million entries on Nginx access logs by parsing unstructured logs into structured columns, optimizing data types, and sorting data by similarity, far surpassing traditional file compression like ZSTD (38x) or raw ClickHouse ingestion (35x).
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<strong>Temporal Joins (5 minute read)</strong>
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Temporal joins, retrieving the latest or nth related record, are often challenging to implement from a functionality (e.g., duplicate rows from timestamp collisions) and performance perspective. PostgreSQL's DISTINCT ON simplifies “last record” joins. For more complex scenarios, CTEs and window functions with ROWNUMBER deliver deterministic results with tie-breaker logic. Use direct SQL over ORMs that tend to implement less optimal patterns for these types of operations.
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<h1><strong>Opinions & Advice</strong></h1>
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<strong>Why I Code as a CTO (6 minute read)</strong>
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John Wang shows that coding keeps technical leaders grounded in reality and more effective in decision-making. By personally shipping experiments, urgent fixes, and bug patches, he maintains deep system understanding and product intuition. Modern AI tools amplify this leverage, letting leaders stay hands-on while focusing on the areas where their expertise drives the most value.
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<strong>Thinking Like a Data Engineer (6 minute read)</strong>
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Data engineering is about translating real-world systems (e.g., shelves, products, and events) into data models, shifting the focus from tools and pipelines to understanding reality. Key mental models include curiosity, observation, iteration, and humility.
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<strong>The Rise of Logical Data Management: Why Now Is the Time to Rethink Your Data Strategy (4 minute read)</strong>
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Due to the limitations of centralized, physical data systems, logical data management can be seen as a solution to unify access, semantics, and governance across distributed data sources. It enables seamless access to all data (cloud, on-premises, SaaS, or streaming) without requiring physical replication or centralization, and represents data in business-friendly terms instead of source-system code, making it easier for AI models and users to use data.
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<strong>Best Practices that Break Data Platforms (5 minute read)</strong>
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Traditional data engineering "best practices" can hinder modern data platforms, particularly in the context of cloud-native systems, rapid data growth, and AI. For example, the mantra of centralizing all data for future use cases leads to complex, unused pipelines, duplicated logic, and untrustworthy data due to missing context. Instead, start with business intent and build focused pipelines backward.
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<h1><strong>Launches & Tools</strong></h1>
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<strong>Aiven Inkless: Diskless Kafka that writes directly to S3 (Sponsor)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Tired of unpredictable Kafka bills and high data retention costs? Inkless implements diskless topics that write data directly to object storage, like AWS S3, using a leaderless architecture where any broker can handle any partition. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Faiven.io%2Finkless%3Futm_source=---%26utm_medium=sponsored%26%26utm_content=tldr/2/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/z1F7pgqGlImANoLVZTdsr6iSPisQU-wxlKcc0UF8dIw=428" rel="noopener noreferrer nofollow" target="_blank"><span>Slash Kafka TCO by up to 80 %</span></a> while staying 100% compatible with every client, connector and tool you already use. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Faiven.io%2Finkless%3Futm_source=---%26utm_medium=sponsored%26%26utm_content=tldr/3/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/_VOZL52WdcTlEfxJt9WziKDdCqc-TBY9CW0vMlgxPAo=428" rel="noopener noreferrer nofollow" target="_blank"><span>Start free</span></a>
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<strong>Bruin (Tool)</strong>
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Bruin is an open-source data engineering platform like dbt, but written in Go, combining SQL and Python for building ELT pipelines with built-in data quality, lineage, and observability. It unifies ingestion, transformation, ML workloads, and governance in one CLI-based system, giving teams faster, simpler, and fully self-hostable control over their entire data platform.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjack-vanlightly.com%2Fblog%2F2025%2F10%2F22%2Fa-fork-in-the-road-deciding-kafkas-diskless-future%3Futm_source=tldrdata/1/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/mznpUf85dKVlBKnjmjs15yWxRCJQkXlJlAsl_joLcdM=428">
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<strong>A Fork in the Road: Deciding Kafka's Diskless Future (16 minute read)</strong>
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Kafka faces two almost opposing forces: low-latency, disk-based workloads, versus elastic, cloud-native (such as analytics) workloads that favor stateless compute and shared object storage. KIP-1150 Revision 1 not only cuts cross-AZ replication costs but fully leverages object storage for a stateless, elastic Kafka architecture, introducing new broker roles and groups to achieve scalability similar to WarpStream and Confluent Freight Clusters.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.tigerdata.com%2Fblog%2Fintroducing-pg_textsearch-true-bm25-ranking-hybrid-retrieval-postgres%3Futm_source=tldrdata/1/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/bXigXVEiPdJlfet8CSjdU-_Zv_IsWd4LwgDZJlCh0MI=428">
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<strong>From ts_rank to BM25. Introducing pg_textsearch: True BM25 Ranking and Hybrid Retrieval Inside Postgres (10 minute read)</strong>
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pg_textsearch is a new PostgreSQL extension purpose-built for AI-native workloads, delivering BM25-ranked keyword search optimized for hybrid retrieval alongside pgvector. It overcomes native Postgres search limitations by supporting IDF weighting, term frequency saturation, and document length normalization, substantially improving relevance and scalability. Fully transactional and SQL-native, pg_textsearch eliminates the need to integrate external search platforms to enable high-quality context retrieval.
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<strong>Kedro (GitHub Repo)</strong>
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Kedro provides an opinionated, extensible framework for building reproducible, modular, and testable data science & analytics pipelines. It emphasizes clean code, data catalog integration, configuration-driven design, and CI/CD readiness and provides a data pipeline visualization module.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhackernoon.com%2Fsystem-design-in-a-nutshell%3Futm_source=tldrdata/1/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/NJwLjvWYU2tY_xKNZmte1fX3rUBvIQcmy5MQpjO5rxA=428">
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<strong>System Design in a Nutshell (10 minute read)</strong>
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System design has three core pillars: scalability (how a system handles growth), reliability (its tolerance for faults), and performance (latency and throughput). Great design isn't about piling on components, it's about making explicit trade-offs by designing for the dominant constraint (latency, scale, cost, or reliability) and evolving as usage shifts. Simple, observable, and loosely coupled systems outperform over-engineered ones in the long run.
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<strong>Announcing General Availability of Neo4j Graph Intelligence for Microsoft Fabric (2 minute read)</strong>
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Neo4j Graph Intelligence is now generally available for Microsoft Fabric, enabling direct execution of advanced graph analytics on OneLake tables with zero administration and seamless, secure Azure integration. Users gain AI-assisted, no-code modeling and can run enterprise-grade graph algorithms for extracting interconnected insights from disparate datasets.
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<h1><strong>Quick Links</strong></h1>
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<strong>Rediscovering the SUPER in Supercomputing (2 minute read)</strong>
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dagster-slurm integrates Dagster's modern data orchestration with Slurm-managed HPC clusters, enabling seamless workflow portability across laptops, CI pipelines, and Tier-0 supercomputers without code changes.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhackernoon.com%2Fthe-illusion-of-scale-why-llms-are-vulnerable-to-data-poisoning-regardless-of-size%3Futm_source=tldrdata/1/0100019a2521d7a5-3239a6ae-0453-4b51-aff3-ba76b8444740-000000/cNWmKacfizhj29jUoUdYxeA-qam1DaGcwoGJW8rfjfI=428">
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<strong>The Illusion of Scale: Why LLMs Are Vulnerable to Data Poisoning, Regardless of Size (5 minute read)</strong>
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New research finds that poisoning large language models doesn't require controlling a large share of the training data.
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