Analytics Engineer
SQL, schema design, transactions, NoSQL tradeoffs, Python data tools, and the performance analysis skills production demands.
CS Fundamentals
SQL
Stub page — SQL content has moved to [[sql/sql-fundamentals]].
CS Fundamentals
Database Design
Designing relational database schemas for correctness, performance, and maintainability. Good schema design prevents bugs, makes queries fast, and reduces the cost of future changes.
CS Fundamentals
Database Transactions
ACID guarantees, isolation levels, deadlocks, and patterns for correct concurrent data access.
↳ Locks, contention, isolation levels
CS Fundamentals
NoSQL Databases
NoSQL covers four distinct families (document, key-value, wide-column, graph) each with different consistency models and query trade-offs. PostgreSQL + Redis is the most common 2026 production stack; NoSQL wins when data shape is variable, write throughput is extreme, or relationship traversal dominates.
↳ When SQL is the wrong tool
CS Fundamentals
Performance Optimisation
Systematic approach to improving system performance: measure first, optimise the bottleneck, measure again.
↳ Query plans, explain analyse, index usage — measure before you optimise
Python
SQLAlchemy 2.0
Python's standard ORM and SQL toolkit. Version 2.0 (2023) introduced fully type-annotated models via `Mapped[T]` + `mapped_column()`, a unified `select()` API, and first-class async support.
↳ SQL from Python
Python
Polars + DuckDB
The two workhorses of Python data processing for AI engineers. Polars is a DataFrame library (pandas replacement) built on Apache Arrow — lazy evaluation, parallel by default, no GIL.
↳ Analytical workloads
Cloud
Data Engineering on Cloud
Building data pipelines and analytics infrastructure on AWS. From S3 data lake to queryable analytics with Athena, Glue, and Redshift.
↳ Streaming vs batch: real-time pipelines vs scheduled jobs
8 pages · ~5h estimated reading time
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