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by HackerNoon
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- 1 weeks ago10 min
Sampling Ad Exchange Events at Scale: What We Kept, What We Dropped, and Why
This story was originally published on HackerNoon at: https://hackernoon.com/sampling-ad-exchange-events-at-scale-what-we-kept-what-we-dropped-and-why. How we reduced Kafka stream volume by 80%, cut Spark workers from 240 to 30, and lowered reporting costs through sampling and executor tuning at Unity. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #apache-kafka, #apache-spark, #big-data, #data-pipelines, #adtech, #cloud-cost-optimization, #data-sampling-techniques, and more. This story was written by: @vikrantbachhav. Learn more about this writer by checking @vikrantbachhav's about page, and for more stories, please visit hackernoon.com. At Unity, our ad exchange was processing approximately 10 GB of Kafka event data per second, much of which wasn't needed for downstream reporting. By introducing auction-level sampling, retaining critical events at full fidelity, and optimizing Spark executor configurations, we reduced stream volume by 80%, cut Spark workers from 240 to 30, and lowered DSP reporting transform costs by approximately 41%. Along the way, we uncovered longstanding reporting inaccuracies and learned why understanding data usage is just as important as optimizing infrastructure.
- 1 weeks ago21 min
83 Blog Posts To Learn About Metrics
This story was originally published on HackerNoon at: https://hackernoon.com/83-blog-posts-to-learn-about-metrics. Learn everything you need to know about Metrics via these 83 free HackerNoon blog posts. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #metrics, #learn, #learn-metrics, and more. This story was written by: @learn. Learn more about this writer by checking @learn's about page, and for more stories, please visit hackernoon.com.
- 1 weeks ago8 min
Why Your Python Cache Does the Same Work Twice (P.S: GIL Won’t Save You)
This story was originally published on HackerNoon at: https://hackernoon.com/why-your-python-cache-does-the-same-work-twice-ps-gil-wont-save-you. Your Python cache can run the same work twice, even with the GIL. See the check-then-act race, the lru_cache gotcha, and how locks fix it. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #python-threading, #race-conditions, #python-gil, #thread-safety, #python-concurrency, #hackernoon-top-story, and more. This story was written by: @sohelalam79. Learn more about this writer by checking @sohelalam79's about page, and for more stories, please visit hackernoon.com. A cache that checks "is this key missing?" and then computes the value can run the same work many times when threads interleave, even with the GIL, because the GIL protects the interpreter, not your two-step logic. Hold one lock around the check and the work, ideally one lock per key.
- 1 weeks ago7 min
AI-Assisted Reconciliation of Enterprise Data: Detecting and Explaining Cross-Platform Discrepancies
This story was originally published on HackerNoon at: https://hackernoon.com/ai-assisted-reconciliation-of-enterprise-data-detecting-and-explaining-cross-platform-discrepancies. Learn how deterministic checks and AI work together to detect data discrepancies, explain their causes, and simplify financial reconciliation. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #data-quality, #ai, #data-reconciliation, #lakehouse, #data-science, #data-regulations, #ai-assisted-reconciliation, and more. This story was written by: @ajaysikharam. Learn more about this writer by checking @ajaysikharam's about page, and for more stories, please visit hackernoon.com. Learn how deterministic checks and AI work together to detect data discrepancies, explain their causes, and simplify financial reconciliation.
- 1 weeks ago3 min
REPL While It Works: Inspect a Running Python Program Without Pausing It
This story was originally published on HackerNoon at: https://hackernoon.com/repl-while-it-works-inspect-a-running-python-program-without-pausing-it. Use the python REPL or debugger console while a program is running, without interrupting or pausing execution. Exploit threading multitasking. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #python-developers, #repl, #python-pdb, #python, #python-inspection, #debugging, #coding, and more. This story was written by: @dakra. Learn more about this writer by checking @dakra's about page, and for more stories, please visit hackernoon.com. Usually, without interrupting it, you cannot interactively debug, call functions in, or change the values of variables in a running python program. This article’s technique removes the limitation.
- 1 weeks ago7 min
When a Dashboard Looks Perfect but the Data Is Wrong
This story was originally published on HackerNoon at: https://hackernoon.com/when-a-dashboard-looks-perfect-but-the-data-is-wrong. Data definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #data-engineering, #data-analytics, #data-visualization, #data-governance, #data-testing, #automated-data-testing, #business-intelligence, and more. This story was written by: @kkj-data. Learn more about this writer by checking @kkj-data's about page, and for more stories, please visit hackernoon.com. Data definitions are crucial for accurate metrics, and their absence can lead to believable but wrong numbers that nobody questions. Centralizing definitions and putting business logic in versioned, tested models can prevent metric disputes and ensure trust in data-driven decisions.
- 1 weeks ago29 min
No One Holds the Key: Threshold ECDSA, Part 1
This story was originally published on HackerNoon at: https://hackernoon.com/no-one-holds-the-key-threshold-ecdsa-part-1. A deep dive to threshold elliptic curve digital signature algorithm (ECDSA) and its building blocks: oblivious transfer, MtA, OT-based protocol. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #multi-party-computation, #ecdsa, #oblivious-transfer, #cryptography, #distributed-key-generation, #elliptic-curve-cryptography, #crypto, #hackernoon-top-story, and more. This story was written by: @privatekeypirate. Learn more about this writer by checking @privatekeypirate's about page, and for more stories, please visit hackernoon.com. Threshold ECDSA lets t-of-n parties produce a standard ECDSA signature without anyone ever holding the full private key. Part 1 covers ECDSA basics and nonce pitfalls, commitment-based DKG that blocks rogue-key attacks, the history of threshold ECDSA, the HLNR18 protocol’s trade-offs, and OT-based multiplication.
- 1 weeks ago31 min
OData at Scale: When to Use CDS Views, RAP, or API Business Hub in Hybrid SAP
This story was originally published on HackerNoon at: https://hackernoon.com/odata-at-scale-when-to-use-cds-views-rap-or-api-business-hub-in-hybrid-sap. Choose between CDS-view OData, custom RAP services, and API Business Hub in hybrid SAP landscapes, with Cloud Connector, security, and performance trade-offs. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #api-development, #hybrid-cloud, #enterprise-technology, #rap, #cds, #odata, #odata-at-scale, and more. This story was written by: @anupbharti. Learn more about this writer by checking @anupbharti's about page, and for more stories, please visit hackernoon.com. Hybrid SAP OData is three decisions, not one: CDS views for read-heavy Fiori/analytics, RAP for anything beyond CRUD, and API Business Hub when an external party is involved — used together. Across the Cloud Connector, tuning changes: cache metadata, watch $expand size, prefer $select calls over deep expands. Propagate identity per user with short-lived certificates, not a shared technical user. A read view rarely stays internal — revisit governance and write questions as it gains consumers.
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