Slowly Changing Dimensions (SCDs) address one of the most practically important data warehouse design challenges: how to handle changes to dimension attributes over time. An IPL player changes teams, a venue changes its naming rights sponsor, a franchise relocates to a new city — how should the data warehouse record these changes? Should historical fact records reflect the attribute value at the time of the event, or the current value? The answer depends entirely on the business question being answered, and different SCD types provide different answers to this question.
Ralph Kimball defined the original SCD types 0, 1, and 2, which remain the most important in practice. Type 3 and Type 4 address specialised scenarios. Each type makes a specific choice about what historical information to preserve. Getting this choice wrong produces incorrect historical analysis: using SCD Type 1 (overwrite current) for player team means that a player's historical deliveries appear under their current team, not the team they were playing for at the time — making historical team performance analysis completely incorrect.
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.