Data & Pipelines
Data and pipelines cover the path a reading takes from the moment a device produces it to the point where a tool can use it. Ingestion receives the stream, alignment reconciles timing across sources, storage retains it, and aggregation condenses raw samples into a session. Each stage is a place where data can be lost, misaligned, or quietly corrupted, which is why this unglamorous plumbing decides how trustworthy everything downstream can be.
Data & Pipelines
Ingestion
Receiving the stream
Ingestion is the entry point where readings arrive from a device, an API, or a file. It must cope with bursts, retries, and partial transfers without losing or duplicating data. Decisions made here, how readings are buffered and acknowledged, shape the reliability of everything that follows.
Alignment
Reconciling time
When data comes from several devices, their clocks rarely agree exactly. Alignment reconciles timestamps so that a heart rate, a position, and a power reading taken at the same instant line up. Without careful alignment, combined metrics drift apart and derived figures become unreliable in ways that are hard to spot later.
Storage
Retaining the record
Storage decides how readings are kept: raw, aggregated, or both, and for how long. The choice balances the value of detail against cost and privacy. Retaining raw data preserves the ability to recompute later; discarding it saves space but forecloses questions that have not been asked yet.
Aggregation
From samples to sessions
Aggregation condenses a dense stream of samples into the summaries people actually read: a session, a lap, a daily total. This stage deliberately discards detail for clarity, and how it does so determines what questions the resulting record can answer. Summaries are convenient but inherit every gap and misalignment beneath them.