Learn the foundations of relational databases — SQL syntax, table design, relationships, and normalization principles.

The shape of data decides the shape of everything downstream — normalization, denormalization, dimensional modeling, and when to break the rules.

Understand how database indexes work — B-tree structures, query planning, EXPLAIN analysis, and how to optimize slow queries.

Databases outlive every application version. Schema migrations — expand/contract, backfills, online schema changes — are the discipline of evolving storage without downtime.

Master database transactions — ACID guarantees, isolation levels, read phenomena, and how databases handle concurrent access.

Explore horizontal scaling strategies — leader-follower replication, multi-leader setups, and sharding techniques for distributed databases.

Survey the NoSQL landscape — document stores, key-value, column-family, and graph databases — and when to choose each over SQL.

The fastest data is the data you already have — caching strategies, invalidation, Redis, browser and edge caches, and why caching is the first lever in every latency budget.

Why columnar stores dominate analytics — column-major layout, compression, vectorised execution, and the timeseries workload that lives on top.

What actually stores your data — B-trees and LSM trees, write-ahead logs, memtables, compaction, and how RocksDB/Cassandra/LSM-based engines optimize for write-heavy workloads.

The analytical data stack — Kimball vs Inmon data warehousing, the data lake, the lakehouse, ETL/ELT, and the trade-offs behind modern table formats (Iceberg, Delta).

Spanner, CockroachDB, TiDB, Calvin — the lineage of systems that offer SQL across geo-distributed nodes with strong consistency and horizontal scale.