This story was originally published on HackerNoon at: https://hackernoon.com/how-i-decoded-my-apple-watch-metrics-taking-a-look-at-the-raw-numbers-part-2.
Learn how to parse Apple Health XML & GPX files. A technical guide to "streaming" large CDA files and extracting workout kinematics using Python.
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This story was written by: @farzon. Learn more about this writer by checking @farzon's about page, and for more stories, please visit hackernoon.com.

Exporting Apple Health data results in massive, messy XML files that are difficult to process. By using a "streaming" parser to filter specific LOINC codes and extracting GPS kinematics from GPX files, I converted 300MB of raw records into clean CSVs. This structured data is now ready to be fed into a custom machine learning model to reverse-engineer VO2 Max.

This story was originally published on HackerNoon at: https://hackernoon.com/why-ai-agents-are-creating-a-new-kind-of-data-engineer.
The role of data engineers is evolving faster than ever and this is the advent of intelligence engineers who will not only build AI agents but create governance
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This story was written by: @engineervarun0012. Learn more about this writer by checking @engineervarun0012's about page, and for more stories, please visit hackernoon.com.

The role of data engineers is evolving faster than ever and this is the advent of intelligence engineers who will not only build AI agents but create governance around them along with strict guardrails.The blog sheds light on the next generation data leader

This story was originally published on HackerNoon at: https://hackernoon.com/the-architectural-limits-of-data-lakes-and-the-rise-of-lakehouses.
Data lakes solve storage but not reliability. Learn how lakehouse architecture adds transactions, metadata, and governance to fix the gap.
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This story was written by: @seshendranath. Learn more about this writer by checking @seshendranath's about page, and for more stories, please visit hackernoon.com.

Raw files on object storage are great for cheap retention but terrible as a system of record lakehouse architecture adds transactional tables, versioned metadata, and schema contracts on top of the same storage, turning a dumping ground into a reliable analytical platform.

This story was originally published on HackerNoon at: https://hackernoon.com/the-economic-case-for-investing-in-youth-education.
Causal studies show youth education investment can deliver strong economic returns, especially in early childhood and low-income countries.
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This story was written by: @dharmateja. Learn more about this writer by checking @dharmateja's about page, and for more stories, please visit hackernoon.com.

Causal studies show youth education investment can deliver strong economic returns, especially in early childhood and low-income countries.

This story was originally published on HackerNoon at: https://hackernoon.com/hivemq-and-timescaledb-it-just-works.
How HiveMQ and MQTT enabled real-time SCADA data streaming to power machine learning and optimize an industrial dosing process at scale.
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This story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page, and for more stories, please visit hackernoon.com.

Using HiveMQ, an industrial plant streamed real-time SCADA data to external machine learning models to fix a failing dosing process. The flexible MQTT pipeline made it easy to add new data inputs without rework. Paired with TimescaleDB, the system scaled to handle continuous telemetry, turning unreliable production into a stable, optimized operation.

This story was originally published on HackerNoon at: https://hackernoon.com/102-blog-posts-to-learn-about-datasets.
Learn everything you need to know about Datasets via these 102 free HackerNoon blog posts.
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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.

This story was originally published on HackerNoon at: https://hackernoon.com/why-more-data-doesnt-guarantee-better-insights-in-modern-data-systems.
More data doesn’t mean better insights. Learn how poor data quality, bias, and pipeline issues undermine analytics at scale.
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This story was written by: @seshendranath. Learn more about this writer by checking @seshendranath's about page, and for more stories, please visit hackernoon.com.

Volume amplifies both signal and defect equally. Pipelines multiply bad measurements, high-dimensional features invite leakage and spurious correlation, and scale can't fix sampling bias it just hardens it. Better insights come from data that's fit for purpose, stable over time, and validated before it reaches downstream consumers. The goal isn't the biggest dataset; it's the smallest one that still preserves the true shape of the problem.

This story was originally published on HackerNoon at: https://hackernoon.com/500-blog-posts-to-learn-about-data.
Learn everything you need to know about Data via these 500 free HackerNoon blog posts.
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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.