Learn SQL, Python, and Data Modeling Interactively

Every record deserves a fingerprint

Topics: The Problem of Identity, Primary Keys: Data Identity, Foreign Keys, Composite Keys, Key Generation Strategies

Choosing the right box for every value

Topics: The FLOAT Money Bug, String Types & Platform Traps, Temporal Types & DST, ENUM Traps, Type Review Framework

How tables talk to each other

Topics: What Are Relationships?, Cardinality Explained, Required vs Optional, Self-Referential Tables, Complex Patterns

Why copying data breaks everything

Topics: Data Gets Out of Sync, First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), Identifying Normal Form

Beyond third normal form

Topics: Boyce-Codd Normal Form, Fourth Normal Form (4NF), Fifth Normal Form (5NF), Strategic Denormalization, Denormalization Patterns

Stars, snowflakes, and facts between

Topics: The Star Schema, Types of Fact Tables, Types of Dimensions, Defining the Grain, Surrogate Keys

When flat tables meet nested reality

Topics: The Nesting Decision, STRUCT: Embedded Objects, ARRAY: Ordered Collections, MAP: Dynamic Key-Value Pairs, Columnar Storage & Nesting

Data that never forgets

Topics: Event-Driven Architecture, Immutable Append-Only Logs, Event Sourcing, Clickstream Modeling, Handling Late-Arriving Data

Pre-computing answers before anyone asks

Topics: Why Pre-Aggregate?, Metric Types and Additivity, OLAP Cubes & Rollups, Granularity Design, Refresh Strategies & Materialized Views

Blueprints for building data systems

Topics: Medallion Architecture, Data Vault, One Big Table (OBT), Semantic Layers, Pipeline DAG Design

Data lives where it is created, not where it is needed; pipelines move and reshape it

Topics: Why Pipelines Exist, The Four Roles in Any Pipeline, Reading a Pipeline Left to Right, A First End-to-End Pipeline, When a Pipeline Is Not Needed

When one pipeline becomes many, the question is not what to build but how the pieces fit

Topics: Many Sources, One Curated Layer, ETL vs ELT, The DAG: Why Dependencies Form, Reading a Real Pipeline Diagram, One Source, Two Different Consumers

Pipelines are products with owners, contracts, and lifecycles, not scripts that move data

Topics: Pipelines as Products, The Cross-Cutting Undercurrents, When to Split, When to Merge, Build vs Buy at Each Layer, Redesigning a Tangled Graph

Data moves in scheduled chunks or in a continuous flow; the choice changes everything downstream

Topics: Two Ways Data Can Move, Batch: Picture, Rhythm, Example, Streaming: Picture, Rhythm, Example, What Real-Time Actually Means, Picking Batch or Streaming

Latency, throughput, state, and cost are the dimensions; pick deliberately, not by default

Topics: Latency vs Throughput Tradeoff, Micro-Batch: The Middle Ground, Why Streaming Costs More, Stateful vs Stateless Transforms, When Batch Outgrows Itself

Lambda, Kappa, and unified engines: architectures live or die on freshness tier discipline

Topics: Lambda Architecture, Kappa: Stream Only, Batch Replay, Unified Engines: Where Lines Blur, Per-Node Freshness Tier Analysis, Lambda to Kappa Worked Example

Different shapes of storage exist because different jobs need different physics

Topics: Storage Is Not Just the Database, The Data Warehouse, The Data Lake, The Operational Database, Picking the Right Storage Shape

Columns, partitions, compression, and pushdown turn a 10TB scan into a 100GB scan

Topics: Columnar Versus Row Storage, Partitioning and File Pruning, Compression: Bytes Versus CPU, Predicate Pushdown, 10TB Versus 100GB: A Worked Example

Open table formats turn object storage into a transactional database without giving up the lake

Topics: The Lakehouse: ACID on Object, Snapshot Isolation and Time Travel, Schema Evolution Without Rewrites, The Small Files Problem, Choosing Storage Across Workloads

What runs, when, in what order, and what happens when something fails

Topics: Why Cron Is Not an Orchestrator, The DAG: Tasks, Edges, No Cycles, What an Orchestrator Does, The Major Orchestrators by Name, First DAG: 3 Tasks, 1 Schedule

Your first lines of Python start here

Topics: Variables and Assignment, Data Types, Print Statements, Basic Operators, Comments

Decisions, loops, and reusable logic

Topics: Conditional Statements, Loops, Functions, Return Values, Variable Scope

Lambdas, comprehensions, and more

Topics: Lambda Functions, List Comprehensions, Decorators, Generators, Context Managers

Where every Python journey begins

Topics: How Computers Store Data, Variables and Naming, Assignment vs. Equality, Data Types and Strings, Operators and Readability

Making decisions with data

Topics: Type Conversions, Comparison Operators, Logical Operators, Multiple Assignment, None and Identity

Patterns for technical interviews

Topics: Multiple Assignment, Short-circuit Evaluation, Truthy and Falsy Values, Ternary Expressions, Walrus Operator (:=)

Making decisions in code

Topics: The if Statement, Branching with if-else, Chaining if-elif-else, Combining with and/or, Execution Flow

Writing cleaner conditional logic

Topics: Guard Clauses, Chained Comparisons, Pattern Matching with match-case, Conditional Assignment, Edge Case Handling

Elegant patterns for complex decisions

Topics: Boolean Simplification, De Morgan's Laws, State Machine Patterns, Dict-Based Dispatch, Decision Table Lookups

Repeating actions efficiently

Topics: Iterating with for Loops, range() Function, Loops with while, Using break and continue, Loop Variable Scope

Your first SQL query — demystified

Topics: Tables, rows, and columns, SELECT and FROM basics, Selecting all columns (*), AS aliases for columns, Expressions in SELECT

CTEs: subqueries are a cry for help

Topics: CTEs (WITH clause), Query Execution Order, Subqueries for temp results, UNION and UNION ALL, ORDER BY and LIMIT

SQL operators nobody warned you about

Topics: Correlated subqueries, EXCEPT and EXCEPT ALL, INTERSECT and INTERSECT ALL, UNNEST for arrays, SELECT Without FROM

INT, VARCHAR, and the lies we tell

Topics: Why data types matter, INTEGER for whole numbers, STRING types (VARCHAR), BOOLEAN for true/false, CAST for type conversion

Where pennies vanish and NULLs defy

Topics: BOOLEAN and NULL logic, DECIMAL precision and scale, TIMESTAMP vs TIMESTAMP WITH TIME ZONE, Time zones and UTC handling, TRY_CAST and implicit casts

Arrays, maps, and type optimization at scale

Topics: Type optimization at scale, Storage calculations and VARCHAR, MAP data type for key-value pairs, Accessing nested data (UNNEST), Compression and error handling

WHERE: your database bouncer

Topics: WHERE clause for filtering rows, Equals and not equals (=, !=), Comparison operators (<, >), IN and NOT IN for list matching, AND for combining conditions

Boolean logic: it's complicated

Topics: OR and CASE expressions, Operator precedence, LIKE for pattern matching, LIMIT and OFFSET for pagination, BETWEEN for range filtering

Subqueries, regex, and other crimes

Topics: Correlated subqueries (EXISTS), NOT EXISTS for missing rows, NOT IN vs NULL gotchas, REGEXP_LIKE patterns, Regex operators and patterns

A million rows walk into a SUM...

Topics: GROUP BY for categorizing data, COUNT variations (*,col,DISTINCT), SUM and AVG calculations, MIN and MAX for extremes, HAVING for filtered groups