Data Science Tech Brief By HackerNoon

This story was originally published on HackerNoon at: https://hackernoon.com/how-we-built-a-per-plant-co2-dataset-for-4551-power-stations-worldwide. An open dataset of 4,551 power stations: measured + modelled CO2, fuel, owner, capacity and climate zone. How we built it in Python, and the honest limits. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #python, #global-energy-monitor, #greenhouse-gas-data, #carbon-accounting, #climate-analytics, #energy-infrastructure, #python-etl, and more. This story was written by: @dmytroah. Learn more about this writer by checking @dmytroah's about page, and for more stories, please visit hackernoon.com. The authors built and openly published a dataset covering 4,551 power stations worldwide, combining emissions, ownership, capacity, fuel type, and climate-zone data into a single schema. The project's central finding is that only about 15% of plant-level emissions data comes from direct measurements, while the remaining 85% relies on modelled estimates, making provenance and transparency critical for anyone working with emissions datasets.

This story was originally published on HackerNoon at: https://hackernoon.com/scaling-self-service-analytics-in-regulated-banking-with-metadata-driven-design. Scaling self-serve analytics in regulated banking is hard. Learn how metadata-driven design enforces governance while letting teams explore data safely Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #bigquery, #gcp, #data-governance, #mlops, #cross-cloud-data-platform, #cloud-data-engineering, #self-service-analytics, and more. This story was written by: @jeevanreddygeeredd. Learn more about this writer by checking @jeevanreddygeeredd's about page, and for more stories, please visit hackernoon.com. Self-service analytics in banking is not primarily a technology challenge. It's a governance challenge. This article explores the design of a metadata-driven analytics platform on GCP that enabled business teams to access trusted financial data without creating new silos. Key lessons include treating lineage as a first-class feature, using semantic layers to enforce consistent business logic, and prioritizing auditability over raw performance in regulated environments.

This story was originally published on HackerNoon at: https://hackernoon.com/i-built-an-open-source-firebase-analytics-alternative-because-i-hit-1m-eventsday-once-too-many. After hitting Firebase Analytics 1M events/day cap during a mobile game softlaunch, I built an open-source self-hosted analytics pipeline. Here's how. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #game-development, #analytics-pipeline, #self-hosted-analytics, #event-streaming, #event-tracking, #product-analytics, #firebase-analytics, and more. This story was written by: @rawbbit. Learn more about this writer by checking @rawbbit's about page, and for more stories, please visit hackernoon.com. A few years ago I was the data engineer on a mobile game soft launch when Firebase Analytics quietly started dropping events past its 1M/day cap. We didn't catch it for days. That experience pushed me to build Rawbbit — an open-source, Apache 2.0, self-hosted analytics pipeline that lands raw events as Parquet in your own object storage. This is the story of why hosted analytics fails at scale, why I chose NATS + Parquet + BigQuery external tables, and what I deliberately left out.

This story was originally published on HackerNoon at: https://hackernoon.com/what-the-real-operating-data-on-ai-agents-tells-me-as-an-investor. Alexander Kopylkov on why AI agents are already running enterprise operations and what the production numbers tell him as an investor. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #ai, #ai-agents, #investing, #ai-in-business, #ai-customer-service, #ai-adoption, #ai-integration, and more. This story was written by: @alexanderkopylkov. Learn more about this writer by checking @alexanderkopylkov's about page, and for more stories, please visit hackernoon.com. Alexander Kopylkov, venture investor, finds that AI agents are already running core business functions at scale. Klarna automated 67% of its customer service with a single AI agent, saving $40 million. The remaining 33% of complex cases still required human judgment. Only 17% of companies have deployed agents so far, with 60% planning to within the next 12 months.Kopylkov sees the real investment opportunity in the governance layer that makes agents safe to operate on real business accounts, not in the agents themselves.

This story was originally published on HackerNoon at: https://hackernoon.com/why-speed-matters-how-performance-in-analytics-saves-business-from-digital-paralysis. Lower compute costs and the evolution of data processing tools have radically changed the approach to analytics. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #big-data-analytics, #data-analytics, #data-science, #data-analysis, #low-code-data-scientist, #ai-for-data-science, #ai-data, #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. Most low-code data analytics tools trade performance for convenience: they break down past a few hundred million rows. Megaladata takes a different approach: a proprietary compute core, in-memory execution, SIMD-level optimizations, and a custom memory manager deliver fast data processing without the cost of big data infrastructure. Real results: a streaming pipeline cut from 20 to 4 minutes, and 400M+ rows processed in 8 minutes on a laptop.