Explain materialization strategy and when to use on-need or computed views.

Explain materialization strategy and when to use on-need or computed views.

• A. Materialize beneficial transforms to improve query performance; use on-need or computed views when freshness is critical.
• B. Materialize all data at all times.
• C. Avoid materialization at all costs.
• D. Materialization only affects storage.

Answer: (A)

Materialization strategy is about balancing performance and freshness by deciding which transforms to precompute and store, and when to refresh them. Precomputing and materializing beneficial transforms lets queries run much faster because the system can serve precomputed results instead of re-running heavy computations. You materialize when a transform is frequently used or expensive to compute, and the performance gains justify the storage and maintenance cost. On-need (compute-on-demand) or computed views come into play when getting the freshest data is important. These views aren’t stored; they’re evaluated at query time, so results reflect the latest data but with higher per-query latency. This approach is ideal when data changes often or staleness is unacceptable, and you’re willing to trade some latency for up-to-date results. So, the best practice is to materialize transforms that yield meaningful performance benefits, and use on-need or computed views specifically for scenarios where freshness is critical.

Materialization strategy is about balancing performance and freshness by deciding which transforms to precompute and store, and when to refresh them. Precomputing and materializing beneficial transforms lets queries run much faster because the system can serve precomputed results instead of re-running heavy computations. You materialize when a transform is frequently used or expensive to compute, and the performance gains justify the storage and maintenance cost.

On-need (compute-on-demand) or computed views come into play when getting the freshest data is important. These views aren’t stored; they’re evaluated at query time, so results reflect the latest data but with higher per-query latency. This approach is ideal when data changes often or staleness is unacceptable, and you’re willing to trade some latency for up-to-date results.

So, the best practice is to materialize transforms that yield meaningful performance benefits, and use on-need or computed views specifically for scenarios where freshness is critical.