The star schema's dimension tables are deliberately wide and denormalised. The snowflake schema takes those dimension tables and normalises them into sub-dimension tables — `dim_venue` splits into `dim_venue`, `dim_city`, and `dim_country` tables linked by foreign keys. This reduces within-dimension redundancy but reintroduces multi-hop joins. Snowflake schemas are appropriate when dimension tables are very large with significant redundancy, but most modern columnar warehouses handle redundancy efficiently enough that the star schema's simpler join structure is preferred for analytical workloads.
Data Vault 2.0 (DV2) is a modelling approach designed for enterprise-scale data warehouses where data arrives from dozens of source systems with competing definitions of the same business entities. DV2 separates raw historical data from business interpretation: Hubs store business keys (the IPL player ID) without descriptive attributes, Links record relationships between Hubs (this player bowled in this match), and Satellites store time-stamped attribute history (the player's team, role, and bowling style as they change). DV2 is append-only and fully auditable — no data is ever deleted or overwritten.
Analogy🏏Cricket
🏏 Think of it like cricket: OLTP is the IPL's live ticketing counter — it handles thousands of simultaneous seat reservations, each requiring a precise single-seat record update with immediate confirmation. Speed per transaction and data consistency under concurrent updates are everything. OLAP is the IPL's season statistics department — it runs complex analytical queries across every ball bowled in every match of every season to produce the published rankings, economy rates, and historical comparisons. No one books a seat through the statistics department, and no broadcaster calls the ticketing counter for Bumrah's career economy rate. The two workloads demand completely different systems. Just as the ticketing counter is built for speed and correctness on one seat at a time and would buckle if asked to tally a decade of attendance mid-sale, an OLTP row-store excels at single-record writes but chokes on full-table aggregation; and just as the statistics department pores over millions of past deliveries but would be hopeless at booking a live seat under contention, the OLAP columnar engine sweeps billions of rows yet is the wrong tool for a fast single-row update. The physical design of each — row-oriented for the counter, columnar for the stats desk — is what makes it superb at its own job and unfit for the other's.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.