Relational modeling: tables, keys and relationships
How to represent the world in tables: primary key, foreign key and the 1:1, 1:N and N:N relationships that structure every database.
What a data professional needs to master at each stage of their career. From relational modeling to real-time data streaming — the concepts that show up in technical interviews and in everyday work, explained directly. Filter by level below.
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How to represent the world in tables: primary key, foreign key and the 1:1, 1:N and N:N relationships that structure every database.
Moving past the simple SELECT: COUNT/SUM/AVG, GROUP BY with HAVING, subqueries and CTEs to answer real questions with data.
Why duplicated data becomes a bug. The three normal forms explained with a single example, and when to denormalize on purpose.
How an index works (the book index analogy), B-tree, when it helps, when it hurts and how to read an execution plan.
Why the transactional system's model doesn't work for analysis. Fact and dimension tables, star schema and data warehouse basics.
Extract, transform and load — or ELT? How data travels from source to report, and why orchestration and idempotency matter.
The four types of NoSQL database, what each one solves, and why 'NoSQL is faster' is the wrong answer in an interview.
When one machine isn't enough: vertical and horizontal partitioning, sharding, the partition key and the cost of cross-shard queries.
Processing data as it arrives, not hour by hour. Batch vs streaming, the event log, and what exactly-once really means.
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