Curriculum

Move from relational foundations to system-level database work.

The path below is a recommendation, not a required bundle. Skip stages you already understand and use each product’s module list to confirm fit.

STAGE 01

Read relational data

Tables, rows, keys, filtering, aggregation, joins, and a clean mental model for query results.

Free Kit
STAGE 02

Design relationships

Entities, cardinality, constraints, normalization, join paths, and schema review.

Vertex Pack
STAGE 03

Compose advanced SQL

CTEs, subqueries, windows, set operations, staged transformations, and plan orientation.

Nexus Series
STAGE 04

Reason about production

Indexes, plans, transactions, isolation, locks, blocking, and evidence-first performance work.

Origin Suite → Prime Suite
STAGE 05

Evolve safely

Backward-compatible schema changes, backfills, constraints, release sequencing, and review.

Vault Suite
STAGE 06

Connect the lifecycle

Design, queries, migrations, operational signals, maintenance, and full-system review.

Peak Suite → Elevate Suite
Topic Map

Seven layers that keep reappearing in database work.

SCHEMA

Relational modeling

Entities, keys, cardinality, normalization, constraints, and table boundaries.

QUERY

SQL construction

Filtering, joins, aggregation, CTEs, subqueries, windows, and decomposition.

PLAN

Execution reasoning

Scans, indexed access, estimates, joins, sorting, selectivity, and evidence.

TX

Transactions

Atomic work, isolation, locks, deadlocks, contention, and application boundaries.

SEC

Database security

Roles, grants, parameterization, secrets boundaries, and least privilege.

MIGRATE

Schema evolution

Compatibility, backfills, constraints, release ordering, and rollback thinking.

OPS

Operational review

Latency, errors, blocking, growth, incidents, and post-release evidence.

Study Workflow

Turn reading into a repeatable experiment.

Use one small relational schema and deliberately alter assumptions as you progress. The goal is to predict behavior, run the smallest useful test, inspect evidence, and explain the result.

01Read one concept cluster

Stop before the topic becomes passive background reading.

02Rebuild the example

Type the SQL yourself and predict the result before execution.

03Break one assumption

Add NULLs, duplicates, different cardinality, or concurrent work.

04Record the evidence

Write the rule, exception, and signal you would use in a real system.

Knowledge Checkpoints

Questions worth being able to answer without guessing.

Schema

  • What fact does this table represent?
  • Which constraints protect real rules?
  • Can the same fact be stored in two places?

Query

  • What is the grain of the output?
  • Can a join multiply rows?
  • Which filters happen before aggregation?

Performance

  • What evidence shows the query is expensive?
  • How selective is the access condition?
  • What write cost would an index add?

Change

  • Can old application code survive this migration?
  • How will a backfill be verified?
  • What happens if deployment stops halfway?
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