When I work with stakeholders, one thing they often tell me I’m good at is taking a one-liner and turning it into a design that works — reliably, at scale, with availability in mind.
Here’s my secret sauce: not just “well-thought of technically design” but data modeling.
The goal isn’t to create the perfect schema on the first try — it’s to iterate fast, improve with every pass, and land on the most robust, efficient design possible within the time you have.
Here’s the checklist I use to go from 0 → 1:
1. Requirements First
🔹 Identify entities (what you track)
🔹 Map relationships (how they connect)
🔹 Capture business rules & edge cases
💡 Ask: “Do I understand every scenario, including the worst possible day for this system?”
2. Conceptual Model (ERD)
🔹 Draw entities, attributes, relationships
🔹 Define cardinality (1-1, 1-many, many-many)
🔹 Model the real world, not legacy quirks
💡 Ask: “Could a non-developer understand the system by just looking at these diagrams?”
3. Schema Design
🔹 Normalize (1NF → 3NF, stop if perf suffers)
🔹 Choose keys (natural vs surrogate)
🔹 Define constraints & plan indexes early
💡 Ask: “Is there redundancy, inconsistency, or a hidden data trap waiting here?”
4. Physical Design
🔹 Plan for growth & access patterns
🔹 Build an index strategy (clustered, composite, covering)
🔹 Partition/shard only if truly needed
💡 Ask: “What happens when we have 10x or 100x data — do we survive or crumble?”
5. SQL Implementation
🔹 Use meaningful names & precise data types
🔹 Add constraints (NOT NULL, CHECK, UNIQUE)
🔹 Document schema & write migration scripts
🔹 Use EXPLAIN to check performance early
💡 Ask: “Would future devs instantly know what this query does — and why it’s written this way?”
6. ORM Integration
🔹 Avoid N+1 queries (use eager loading wisely)
🔹 Drop to raw SQL where it’s justified
🔹 Configure pooling & caching
💡 Ask: “Where do we let the ORM help us — and where do we take control for performance?”
7. Continuous Optimization
🔹 Profile queries regularly
🔹 Add missing indexes, denormalize where helpful
🔹 Scale with replicas, archiving, sharding as needed
💡 Ask: “Have we actually measured improvement, or are we just guessing?”
Pro Tips:
🔹 Model your favorite app’s DB for practice
🔹 Review query plans monthly
🔹 Join r/Database or local meetups for war stories
💡 Ask: “What’s the next bottleneck I should pre-empt before it bites me?”
That's all, following these seven steps will insure your data-model will drive the speed of development not hinder it.
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