Database Mastery Roadmap
From SQL Foundations to Distributed Systems and Vector Search
Your Journey at a Glance
💡 How to use this roadmap
Work through each phase in order. Click on a skill to expand it — you'll find a description and curated resources. Don't rush; understanding beats speed. Complete one phase before moving to the next.
Foundations & Relational Modeling
The fundamentals of data storage. Learn how to structure data for correctness and flexibility before worrying about performance.
Storage Engines & ACID Internals
How do databases actually store bytes on disk? Learn the difference between B-Trees and LSM Trees, and how crash recovery works.
Query Optimization & Indexing
The art of making queries fast. Learn how the database 'thinks' (Planner/Optimizer) and how to influence its decisions.
Concurrency Control & MVCC
Handling multiple users at once. Learn how databases isolate transactions and solve the 'Dirty Read' or 'Lost Update' problems.
Distributed Data Foundations
What happens when data spans multiple machines? Master the fundamental theorems and replication patterns of distributed systems.
Sharding & Horizontal Scaling
Scaling to millions of users. Learn how to partition data across clusters and handle cross-shard queries.
Specialized NoSQL Engines
Not everything is a table. Learn Graph, Wide-Column, and Key-Value stores for specialized business requirements.
Modern Vector Databases & AI
The newest frontier. Learn how to store, index, and search high-dimensional vectors for GenAI and RAG pipelines.
OLAP & Columnar Analytics
Analyzing petabytes of data. Learn how columnar storage and vectorized execution enable 1000x faster analytics than OLTP.
Streaming Data & CDC
Moving data in real-time. Learn how to turn your database into an event stream and sync it across the enterprise.
Database Reliability (DRE)
Running databases in production. Master Backups, PITR, Monitoring, and the art of the 'Zero Downtime Migration'.
NewSQL & Future Frontiers
Where is data going? Master Serverless databases, Distributed SQL, and the next decade of data infrastructure.
Roadmap Complete!
You now have the foundations of a production-ready Java engineer. Apply by building real projects.
Design a Petabyte-Scale Real-Time Analysis Engine
Architect a system that ingests millions of events per second, stores them for archival, provides sub-second semantic search (AI), and allows real-time analytical dashboards.
What you'll build
- Multi-region PostgreSQL for high-availability OLTP (transactions)
- CDC (Debezium) pipeline to sync data between OLTP and OLAP (ClickHouse)
- Vector Search (pgvector/Pinecone) for semantic log analysis and AI querying
- Zero-downtime schema migration pipeline using Flyway and Expand-Contract
- Global read-replicas with GeoDNS routing and latency SLOs
- Tiered storage: Hot data in SSDs, Cold data in S3 via Apache Iceberg
Tech stack
Key highlights
- ✦Demonstrates mastery of Polyglot Persistence (SQL + NoSQL + Vector)
- ✦Covers the entire data lifecycle: Ingestion → Transaction → Analytics → AI Search
- ✦Addresses production concerns: Scalability, Latency, Reliability, and Cost
Real-World Scenarios
Practical case studies where these skills are applied.
01Inventory Race Conditions at Scale
The Problem
Handling a 'Flash Sale' where 1 million users try to buy 100 units of an item simultaneously, leading to negative stock or database lock contention.
The Solution
Implemented 'optimistic locking' with version checks or 'Select for Update' with skip locked. Moved hot-counter de-increments to Redis with atomic Lua scripts for sub-millisecond response.
Outcome
02PostgreSQL Index Bloat & Performance Degradation
The Problem
A high-churn table (10M updates/day) became sluggish despite having indexes. Re-indexing fixed it temporarily, but it regressed every 48 hours.
The Solution
Identified 'HOT' (Heap-Only Tuple) update failures. Adjusted `fillfactor` to 80% to allow space for in-page updates and tuned autovacuum to be more aggressive on high-churn tables.
Outcome
03Migrating a Billion-Row Table with Zero Downtime
The Problem
Adding a NOT NULL column with a default value to a 500GB table in PostgreSQL would lock the table for hours, causing a total outage.
The Solution
Used the 'Expand-Contract' pattern: Add nullable column → Update app to write to both → Backfill data in batches → Add Not Null constraint with 'NOT VALID' → Validate constraint → Drop old logic.
Outcome