Think Like a Data Architect: The 3 States of Data

Описание: States of Data for Data Engineers — in this video we’ll make “data in motion vs data at rest vs data in use” feel so clear that you can explain it calmly on a whiteboard, and actually use it when you design pipelines, debug incidents, or discuss architecture.

Most beginners learn tools first — Spark, Airflow, dbt, SQL, a warehouse, a lakehouse. But senior engineers quietly think in something more fundamental:

What state is the data in right now?
Is it moving between systems? Is it stored somewhere? Or is it being actively processed / queried / viewed?

That single lens changes how you reason about:

reliability and failure modes (timeouts, retries, duplicates, corruption, wrong joins)
performance and cost (throughput vs storage layout vs query behavior)
security and risk (in-flight protection vs storage access vs what’s exposed during compute)
monitoring and observability (lag vs completeness vs runtime / query latency)

What you’ll learn in this video

You’ll understand the three states of data in a simple, practical way:

1) Data in motion
Any time data is traveling between systems over a network — ingestion, API pulls, CDC, BI query results coming back, streaming events, file transfers.

2) Data at rest
Any time data is stored and sitting somewhere — tables, Delta/Parquet files, backups, logs in object storage, partitions waiting to be read.

3) Data in use
Any time something is actively touching the data — a Spark job in memory doing joins/aggregations, a warehouse engine executing a query, a dashboard rendering results on screen, a model loading features to compute a prediction.

The one end-to-end example (so it sticks)

We don’t list ten random examples. We follow one order event through a real flow:

Customer → App → Operational DB → Ingestion → Raw storage → Transform/model → Warehouse → BI dashboard

And at each step, we label the dominant state:

in motion when it travels
at rest when it’s stored
in use when compute (or a human) is actively working with it

Once you see that clearly, you’ll start spotting design decisions instantly:

“This problem is an in motion problem” (retries, idempotency, ordering, backpressure)
“This is a data at rest problem” (format, partitioning, schema evolution, recovery)
“This is a data in use problem” (query planning, joins, caching, concurrency, compute sizing)

Why this matters technically (not just interviews)

If you’re building data systems, you’re constantly making tradeoffs. This mental model forces you to be specific:

Are you optimizing transfer and latency? (in motion)
Are you optimizing storage layout and long-term risk/cost? (at rest)
Are you optimizing computation and access patterns? (in use)

And when something breaks, you debug faster because you stop guessing randomly. You narrow down where the failure belongs — which state is dominant — and you apply the right guardrails in the right place.

A simple exercise you can do today

Take one pipeline you already know: source → ingest → store → transform → serve → dashboard
Write it as boxes, and label each step: motion / rest / use.

Do it once and you’ll start thinking like a system designer, not just a tool operator.

If you’re preparing for senior interviews

This topic shows up everywhere in system design:

“Walk me through the end-to-end flow.”
“Where does the data live?”
“What happens when it moves, when it’s stored, and when it’s queried?”
If you can answer using motion / rest / use, you’ll sound structured, calm, and senior.

CTA
If this video helped you, like and subscribe — the next video builds directly on this:
“How to protect data in each state (in motion, at rest, in use)” in a way that’s simple and interview-ready.

And comment this (just one line):
Which state is easiest for you — motion, rest, or use — and which one feels most confusing right now?
Your answers will tell me where to go deeper next.

#DataEngineering #DataArchitecture #systemdesign

What are the states of data in data engineering?
What is data in motion vs data at rest vs data in use?
How do you explain data in motion and data at rest in interviews?
Why do senior data engineers think about states of data?
How do you map a data pipeline to motion/rest/use?
What are common failure modes for data in motion?
What are common failure modes for data at rest and data in use?
How do retries and idempotency relate to data in motion?
How does partitioning relate to data at rest?
How do joins and aggregations relate to data in use?


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