Data Science Tech Brief By HackerNoon

Data Science Tech Brief By HackerNoon

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Data Science Tech Brief By HackerNoon episodes

  • 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.

    11 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.

    22 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.

    9 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.

    8 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.

    4 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.

    8 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.

    30 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.

    32 min
  • Your Drift Dashboard Can Be Green While Your Model Is Failing

    This story was originally published on HackerNoon at: https://hackernoon.com/your-drift-dashboard-can-be-green-while-your-model-is-failing.


    Build a Python drift monitor with SciPy and Evidently, then test five scenarios showing why data drift doesn't always mean model performance is degrading.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-drift-detection, #ai-data-documentation, #model-degradation, #python-scipy, #concept-drift, #credit-risk-modelling, #ml-model-calibration, #mlops, and more.


    This story was written by: @davidtaiwobalogun. Learn more about this writer by checking @davidtaiwobalogun's about page,
    and for more stories, please visit hackernoon.com.


    Using a synthetic credit-risk model, the author builds a drift-monitoring pipeline with SciPy, pandas, and Evidently and tests it across five controlled scenarios. The results demonstrate that harmless distribution changes can trigger alerts, genuine risk shifts may not require retraining, and concept drift can degrade performance without changing input distributions. The article proposes a monitoring approach combining statistical drift, prediction changes, data-quality checks, and delayed outcome evaluation.

    16 min
  • Why Faster Algorithms Aren't Enough to Build High-Performance Data Platforms

    This story was originally published on HackerNoon at: https://hackernoon.com/why-faster-algorithms-arent-enough-to-build-high-performance-data-platforms.


    Why some tables should never exist: how a data engine cuts copies, memory use, and wasted CPU, from node-to-node access to what the browser actually draws.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-engineering, #software-architecture, #performance-optimization, #memory-management, #parallel-computing, #in-memory-computing, #data-visualization, #good-company, and more.


    This story was written by: @megaladata. Learn more about this writer by checking @megaladata's about page,
    and for more stories, please visit hackernoon.com.


    High-performance data processing depends on more than efficient algorithms or powerful hardware. Drawing on decades of analytical platform development, the Megaladata team explains how data representation, lazy evaluation, reduced copying, specialized memory allocators, and parallel execution influence system performance. The article also explores the trade-offs of Python multiprocessing and the importance of optimizing data transfer and visualization in the browser, arguing that performance must be designed across the entire data lifecycle.

    20 min

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