Object Storage (S3 & Azure Blob)
maang.io System Design Series
Object storage (Amazon S3, Azure Blob) keeps data as objects — a blob plus rich metadata — in a flat namespace addressed by key, not a file tree. It trades filesystem semantics for near-infinite scale and eleven-nines durability via replication/erasure coding. It's the substrate under data lakes, backups, static hosting, and big-data pipelines.
This deep dive comes in three parts:
- Foundations & Core Concepts — objects vs block/file storage and the key-based model.
- Internals — durability, consistency, storage tiers, and multipart upload, with worked numbers.
- Interview — Staff/Principal Q&A: when object storage fits and its consistency guarantees.
Object Storage (S3 & Azure Blob) — Part 3: System Design & Interview Mastery
maang.io System Design Series
What is this?
You now know the coat check from the counter to the back room — objects and keys, the flat namespace, replication vs erasure coding, the strongly-consistent index. This chapter turns that into interview points. Object storage shows up in almost every system-design prompt that involves files, images, video, logs, or analytics — and the interviewer isn't testing whether you can spell "S3." They're testing whether you'll put the bytes in the right place, keep them off your app servers, and reason about durability, consistency, and cost when they push.
We'll do two things: see where object storage anchors real designs (with diagrams), then run a Staff/Principal mock — for each question, what they're really asking, a model answer, and the senior line that signals you've shipped this for real.
The one-line idea: interviewers use object storage to see whether you instinctively separate bulk bytes from queryable metadata — blobs in the object store (fetched by key, served via CDN, uploaded via presigned URL), the small searchable facts in a database — and whether you can defend the durability/consistency/cost trade-offs of that split.
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