Data Science Tech Brief By HackerNoon

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Learn the latest data science updates in the tech world.

  1. How to Optimize Your Marketing Budget Using Just Three Letters: MMM

    4D AGO

    How to Optimize Your Marketing Budget Using Just Three Letters: MMM

    This story was originally published on HackerNoon at: https://hackernoon.com/how-to-optimize-your-marketing-budget-using-just-three-letters-mmm. Marketing Mix Modeling is a statistical analysis method used in marketing to determine the optimal allocation of resources. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #marketing-analytics, #machine-learning, #marketing, #marketing-budget, #marketing-mix-modeling, #media-mix-modelling, #adstock-and-saturation, and more. This story was written by: @radiokocmoc_l45iej08. Learn more about this writer by checking @radiokocmoc_l45iej08's about page, and for more stories, please visit hackernoon.com. Marketing Mix Modeling is a statistical analysis method used in marketing to determine the optimal allocation of resources. The goal of media mix modelling is to understand the impact of different marketing channels on the overall campaign effectiveness. Join me to discover how to optimise the marketing budget by implementing Robyn MMM.

    7 min
  2. Here's How ShareChat Scaled Their ML Feature Store 1000X Without Scaling the Database

    4D AGO

    Here's How ShareChat Scaled Their ML Feature Store 1000X Without Scaling the Database

    This story was originally published on HackerNoon at: https://hackernoon.com/heres-how-sharechat-scaled-their-ml-feature-store-1000x-without-scaling-the-database. How ShareChat scaled its ML feature store to 1B features/sec on ScyllaDB, achieving 1000X performance without scaling the database. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #sharechat-ml-feature-store, #scylladb-scaling-case-study, #ml-feature-store-optimization, #sharechat-moj, #low-latency-ml-infrastructure, #scylladb-database-optimization, #p99-conf-sharechat-talk, #good-company, and more. This story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page, and for more stories, please visit hackernoon.com. ShareChat scaled its ML feature store from failure at 1M features/sec to 1B features/sec using ScyllaDB optimizations, caching hacks, and relentless tuning. By rethinking schemas, tiling, and caching strategies, engineers avoided scaling the database, cut latency, and boosted cache hit rates—proving performance engineering beats brute-force scaling.

    13 min
  3. Why You Shouldn’t Judge by PnL Alone

    5D AGO

    Why You Shouldn’t Judge by PnL Alone

    This story was originally published on HackerNoon at: https://hackernoon.com/why-you-shouldnt-judge-by-pnl-alone. PnL can lie. This hands-on guide shows traders how hypothesis testing separate luck from edge, with a Python example and tips on how not to fool yourself. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #quantitative-research, #trading, #algorithmic-trading, #pnl, #udge-pnl, #profit-and-loss, #judge-profit-and-loss, #hackernoon-top-story, and more. This story was written by: @ruslan4ezzz. Learn more about this writer by checking @ruslan4ezzz's about page, and for more stories, please visit hackernoon.com. I’ve spent years building and evaluating systematic strategies across highly adversarial markets. When you iterate on a trading system, PnL is the goal but a terrible day-to-day signal. It’s too noisy, too path-dependent, and too easy to cherry-pick. A simple framework—form a hypothesis, measure a test statistic, translate it into a probability under a “no-effect” world (the p-value)—helps you avoid false wins, iterate faster, and ship changes that actually stick. Below I’ll show a concrete example where two strategies look very different in cumulative PnL charts, yet standard tests say there’s no meaningful difference in their average per-trade outcome. I’ll also demystify the t-test in plain language: difference of means, scaled by uncertainty.

    13 min
  4. From "Decentralized" to "Unified": SUPCON Uses SeaTunnel to Build an Efficient Data Collection Frame

    6D AGO

    From "Decentralized" to "Unified": SUPCON Uses SeaTunnel to Build an Efficient Data Collection Frame

    This story was originally published on HackerNoon at: https://hackernoon.com/from-decentralized-to-unified-supcon-uses-seatunnel-to-build-an-efficient-data-collection-frame. SUPCON dumped siloed data tools for Apache SeaTunnel—now core sync tasks run 0-failure! Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #bigdata, #apacheseatunnel, #supcon, #data-sync, #high-availability, #data-engineering, #cdc, #hackernoon-top-story, and more. This story was written by: @williamguo. Learn more about this writer by checking @williamguo's about page, and for more stories, please visit hackernoon.com. 99% lower failures, 100% consistency, 70% less O&M cost. Big thanks to @ApacheSeaTunnel!

    16 min
  5. Enterprise Data Pipeline Revolution: Suresh Palli's Metadata-Driven Automation Success

    SEP 19

    Enterprise Data Pipeline Revolution: Suresh Palli's Metadata-Driven Automation Success

    This story was originally published on HackerNoon at: https://hackernoon.com/enterprise-data-pipeline-revolution-suresh-pallis-metadata-driven-automation-success. Suresh Palli revolutionized enterprise data pipelines with metadata-driven automation, cutting dev time 40% and boosting scalability 5x. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #suresh-palli, #metadata-driven-automation, #enterprise-data-pipelines, #data-pipeline-automation, #metadata-governance, #enterprise-data-architecture, #scalable-data-processing, #good-company, and more. This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com. Suresh Palli led a metadata-driven automation project that cut pipeline development time by 40% and scaled data processing 5x. His centralized metadata governance enabled dynamic adaptation, seamless orchestration, and cross-unit alignment. The success earned industry recognition, consulting opportunities, and set new benchmarks for enterprise data automation.

    8 min
  6. Unified Data, Smarter Agents—Is Your Architecture Future-Proof?

    SEP 18

    Unified Data, Smarter Agents—Is Your Architecture Future-Proof?

    This story was originally published on HackerNoon at: https://hackernoon.com/unified-data-smarter-agentsis-your-architecture-future-proof. A hands-on guide to architecting unified, governed and AI-ready data platforms using open table formats, semantic layers and multicloud governance. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #big-data-analytics, #product, #ai, #etl, #azure, #aws, #data-engineering, and more. This story was written by: @@QueryAndConquer. Learn more about this writer by checking @@QueryAndConquer's about page, and for more stories, please visit hackernoon.com. A hands-on guide to architecting unified, governed and AI-ready data platforms using open table formats, semantic layers and multicloud governance.

    8 min
  7. Data-Driven Decisions at Scale: A/B Testing Best Practices for Engineering & Data Science Teams

    SEP 18

    Data-Driven Decisions at Scale: A/B Testing Best Practices for Engineering & Data Science Teams

    This story was originally published on HackerNoon at: https://hackernoon.com/data-driven-decisions-at-scale-ab-testing-best-practices-for-engineering-and-data-science-teams. Ship features like scientists: randomize, measure, and learn fast. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #big-data, #experimentation, #experimental-design, #product-development, #software-engineering, #machine-learning, #statistics, and more. This story was written by: @sayantan. Learn more about this writer by checking @sayantan's about page, and for more stories, please visit hackernoon.com. Ship features like scientists: randomize, measure, and learn fast. Good A/B tests aren’t just stats — they’re the engine driving smarter products.

    6 min
  8. Why You Should (Almost) Always Choose Sync Gunicorn Workers

    SEP 17

    Why You Should (Almost) Always Choose Sync Gunicorn Workers

    This story was originally published on HackerNoon at: https://hackernoon.com/why-you-should-almost-always-choose-sync-gunicorn-over-workers-ze9c32wj. Anyone working on a WSGI web application frameworks like Flask would know that as a best practice it is very important to use a WSGI HTTP Server like Gunicorn to deploy the app outside your development servers. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #gevent, #gunicorn, #python-web-development, #flask, #flask-deployment, #latest-tech-stories, #what-are-gunicorn-worker-types, and more. This story was written by: @shamik-ray. Learn more about this writer by checking @shamik-ray's about page, and for more stories, please visit hackernoon.com. Gunicorn is a widely popular WSGI Server and its popularity is because it is lightweight, fast, simple yet can support most of the requirements you would have to host an app on production. The default worker type is Sync and I will be arguing for it. Async workers like Gevent create new greenlets (lightweight pseudo threads) Every time a new request comes they are handled by greenlets spawned by the worker threads. At the same time, the resources needed to serve the requests will be less.

    6 min
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Learn the latest data science updates in the tech world.

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  • Creator
    HackerNoon
  • Years Active
    2023 - 2025
  • Episodes
    137
  • Rating
    Clean
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    © 2025 HackerNoon