Databases

Beginner database concepts: what a DBMS is, the database landscape, the relational model, keys and constraints, relationships, ER diagrams, normalization and denormalization, data types, NULL semantics, OLTP vs OLAP, and how to choose a database.

SQL from beginner to advanced: logical query order, filtering, joins, aggregation, subqueries, CTEs and recursive CTEs, window functions, set operations, DML with upsert and RETURNING, dialect differences, and common mistakes. Every example was run.

Twenty classic SQL interview problems with executed solutions: second highest salary, top N per group, median, gaps and islands streaks, retention, date series, overlapping and merged intervals, deduplication, pivot and unpivot, plus a pattern map.

How database indexes work and how to make queries fast: B-tree lookups, index types, composite index column order, covering and partial indexes, reading EXPLAIN, sargability, statistics, N+1, pagination, and index maintenance. Query plans were executed.

Database transactions from ACID to advanced: isolation levels and anomalies, MVCC, locking, SELECT FOR UPDATE and SKIP LOCKED, deadlocks, optimistic and pessimistic concurrency, write skew, inventory and booking patterns, durability with the WAL, and retries.

How databases work inside: architecture, pages and the buffer pool, B+trees, LSM trees with a runnable toy implementation, write-ahead logging and crash recovery, query planning, join algorithms, execution models, columnar storage, and probabilistic data structures.

Running databases at scale: replication and lag, high availability and failover, connection pooling, partitioning, sharding with Citus and Vitess, backups and point-in-time recovery, zero-downtime migrations, monitoring, security, and managed services.

NoSQL in depth: Redis and Valkey data structures and patterns, MongoDB document modeling and sharding, DynamoDB single-table design and global tables, Cassandra data modeling and consistency levels, search, graph, time-series and vector databases, and how to choose.

Practical schema design patterns: the modeling process, order snapshots and money, RBAC, hierarchies (adjacency list, closure table, materialized path), history and SCD Type 2, double-entry ledgers, multi-tenancy, JSON attributes, and star-schema analytics. SQL examples were executed.

Database interview preparation: a 60-question bank from beginner to advanced with pointers, schema design prompts, production incident scenarios with a triage flowchart, cheat sheets, common mistakes, and 4-week and 7-day study plans.