SQL
sql
System Design
SQL vs NoSQL - Choosing the Right Database
SQL vs NoSQL is the most common storage decision in system design interviews. SQL databases give you ACID guarantees, joins, and a fixed relational schema; NoSQL databases give you flexible schemas, horizontal scaling, and specialized data models. This lesson teaches you the four NoSQL families, the real engineering trade-offs, and a clear decision framework so you can defend your database choice in any interview.
Database Indexing & Query Optimization
Indexes turn O(N) full-table scans into O(log N) lookups, but every index costs storage and slows writes. This lesson teaches how B-tree and hash indexes work, when to use composite or covering indexes, how to read an EXPLAIN plan, and the common indexing mistakes that cause production outages. By the end you can defend any indexing decision in an interview and diagnose a slow query in production.
Database Sharding & Partitioning Strategies
Sharding splits a database into many smaller pieces (shards) so writes and storage can scale across servers. The hard part is not the splitting; it is choosing a shard key that avoids hot shards, supporting cross-shard queries, and rebalancing as the data grows. This lesson covers the four sharding strategies, how to pick a shard key, the operational realities of resharding, and when sharding is the wrong answer.
Data Warehousing, Data Lakes & OLAP vs OLTP
OLTP databases are built for fast single-row reads and writes; analytical queries against them choke. This lesson covers why analytics needs its own storage stack: column-oriented warehouses, lake formats, and lakehouse engines that scan billions of rows in seconds. You'll learn the OLTP versus OLAP trade-off, dimensional modeling (star schema), ETL versus ELT, change data capture, and how a modern data platform separates compute from storage so you can query petabytes for the cost of a coffee.
Community
Transaction Isolation Levels with Failing Examples
Read uncommitted, read committed, repeatable read, serializable. Each level explained with a runnable two-session SQL example showing exactly which anomaly it allows or prevents.
Data Engineer Loop: The SQL Test That Wasn't Just SQL
A data engineering loop at a Series C analytics company. The SQL round looked like a standard SQL test. It was grading something else entirely.
PostgreSQL MVCC and Isolation Level Deep Dive
A 5-question reference set on PostgreSQL's MVCC implementation: tuple versioning, READ COMMITTED vs REPEATABLE READ vs SERIALIZABLE, row-level locks, and the autovacuum machinery that keeps txid wraparound at bay.
SQL vs NoSQL: Stop Asking the Wrong Question
The choice that matters is not the data model. It is which guarantees you need on read and write. A decision table and the JSONB middle ground that retired half my Mongo use cases.
Database Migrations: A Zero-Downtime Playbook
Adding a column, renaming a column, dropping a column, splitting a table. The expand-contract pattern, the four-step rename, and the migration phases that have kept me from taking the site down.
Database Indexes Explained with Real EXPLAIN Output
What an index actually is, how the planner picks one, and the EXPLAIN output I read every day. Postgres examples, real numbers, and the three indexing mistakes I keep finding in code review.
Data Engineering Pipeline Questions I Prep
Four pipeline-shaped questions I rehearse before data-engineering loops: incremental ingestion, idempotent upserts, late-arriving data, and a SQL window-function read. Python throughout, light on framework lock-in.