エピソード

  • Why Knowledge Graphs and Ontologies Are Making a Comeback in the Age of AI
    2026/10/15

    This story was originally published on HackerNoon at: https://hackernoon.com/why-knowledge-graphs-and-ontologies-are-making-a-comeback-in-the-age-of-ai.
    Context runs the spectrum of semantics, state, and controls.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #knowledge-graph, #context-graph, #artificial-intelligence, #agentic-ai, #ontology, #data-science, #data-modeling, #graph-rag, and more.

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

    Knowing what your data means is the foundation. Getting people and agents to act on that meaning, and proving that it was allowed, is building on the foundation. Context runs the spectrum of semantics, state, and controls.

    続きを読む 一部表示
    38 分
  • How to Solve Blind Spots in Your Architecture by Building a Production-Grade Uptime SaaS
    2026/10/22

    This story was originally published on HackerNoon at: https://hackernoon.com/how-to-solve-blind-spots-in-your-architecture-by-building-a-production-grade-uptime-saas.
    In this article, I will walk you through how I designed, built, and tested a production-grade Uptime & API Health Monitor Micro-SaaS entirely in Python.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #software-engineering, #rest-api, #python, #devops, #backend, #authentication, #architecture, and more.

    This story was written by: @himashitech6978. Learn more about this writer by checking @himashitech6978's about page, and for more stories, please visit hackernoon.com.

    A developer can build their own uptime monitoring service using Python, instead of paying for a third-party service, by following a tutorial that walks through designing, building, and testing a production-grade micro-SaaS. The service features asynchronous health checks, background scheduling, database persistence, and enterprise-grade JWT authentication, all wrapped up in a clean FastAPI backend.

    続きを読む 一部表示
    4 分
  • Stop Using For-Loops for Everything: Advanced Python Iteration Patterns You Should Know.
    2026/10/19

    This story was originally published on HackerNoon at: https://hackernoon.com/stop-using-for-loops-for-everything-advanced-python-iteration-patterns-you-should-know.
    Learn how to write cleaner Python with enumerate, zip, generator expressions, itertools, and functional tools for readable, memory-efficient data processing.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #code-quality, #contest-tags, #python-iteration-techniques, #python-enumerate-and-zip, #python-itertools-tutorial, #python-lazy-evaluation, #hackernoon-top-story, and more.

    This story was written by: @himashitech6978. Learn more about this writer by checking @himashitech6978's about page, and for more stories, please visit hackernoon.com.

    Manual loops can make Python data transformations verbose and harder to maintain. This guide explores four ways to write more idiomatic code using enumerate() and zip() for iteration, generator expressions for lazy evaluation, itertools for efficient data pipelines, and functools.reduce() for aggregation. These tools can improve readability and reduce memory use when applied appropriately, helping developers express what they want to do rather than managing every iteration step by hand.

    続きを読む 一部表示
    4 分
  • One Rogue pip install Away from a Breach
    2026/10/22

    This story was originally published on HackerNoon at: https://hackernoon.com/one-rogue-pip-install-away-from-a-breach.
    Stop Using pip Install Blindly: Securing Your Python Supply Chain in 2026
    Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #secure-software-development, #secure-infrastructure, #supply-chain, #software-engineering, #software-development, #backend-development, #pip-install, and more.

    This story was written by: @himashitech6978. Learn more about this writer by checking @himashitech6978's about page, and for more stories, please visit hackernoon.com.

    To secure your Python application, use deterministic builds with tools like Poetry or pip-tools to lock exact versions and cryptographic hashes. Automate vulnerability scans in your CI/CD pipeline with tools like pip-audit or Safety to catch CVEs before deployment.

    続きを読む 一部表示
    5 分
  • How I Fixed a Data Ingestion Bottleneck With Date Partitioning
    2026/10/12

    This story was originally published on HackerNoon at: https://hackernoon.com/how-i-fixed-a-data-ingestion-bottleneck-with-date-partitioning.
    I fixed a recurring data ingestion bottleneck by working with a vendor to introduce date-partitioned exports and reduce unnecessary file discovery.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #distributed-systems, #data-ingestion-pipelines, #data-partitioning, #object-storage, #incremental-data-ingestion, #idempotent-data-processing, #data-pipeline-reliability, and more.

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

    Our vendor-data ingestion jobs were repeatedly enumerating a growing history of export files. I worked with the vendor's engineers on date-partitioned delivery and prepared our consumer in parallel. This case study explains how partitioning bounds file discovery, why event-date partitions need late-arrival and replay policies, and how to coordinate the producer-consumer cutover.

    続きを読む 一部表示
    9 分
  • Building an Auditable Pipeline for Cross-Language News Coverage Analysis
    2026/10/13

    This story was originally published on HackerNoon at: https://hackernoon.com/building-an-auditable-pipeline-for-cross-language-news-coverage-analysis.
    How I turned 2,565 Arabic news reports on Lebanon’s war into 234 auditable incidents with Python, Media Cloud, and a traceable data pipeline.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #data-pipeline, #media-analysis, #data-provenance, #cross-language-information, #python-data-pipelines, #temporal-data-normalization, #data-reconciliation, and more.

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

    The project aimed to connect international news coverage to specific fatal strikes in a small Lebanese town by reconstructing what had happened accurately enough to search for it in international news databases. The pipeline was built to turn a pile of Arabic news bulletins into a clean, traceable chain of incidents, locations, and dates.

    続きを読む 一部表示
    29 分
  • 224 Blog Posts To Learn About Web Scraping
    2026/10/01

    This story was originally published on HackerNoon at: https://hackernoon.com/224-blog-posts-to-learn-about-web-scraping.
    Learn everything you need to know about Web Scraping via these 224 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 #web-scraping, #learn, #learn-web-scraping, 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.

    続きを読む 一部表示
    55 分