Why Materialized Views Win at TB Scale

Pre-aggregated materialized views transform terabyte-scale analytics by caching computed results. Instead of scanning entire fact tables on every dashboard load, queries read megabytes of pre-computed aggregations. This architectural shift delivers two critical benefits: query latency drops from minutes to sub-second response times, and compute costs plummet since BigQuery charges per slot-second and bytes scanned. The optimization compounds when dozens of dashboards hit the same aggregation patterns. Each query leverages the same cached materialization rather than re-scanning raw data.

Incremental Refresh Mechanics

CREATE MATERIALIZED VIEW sales_daily_agg
PARTITION BY DATE(order_date)
AS
SELECT
  DATE(order_date) as order_date,
  product_id,
  SUM(revenue) as total_revenue,
  COUNT(order_id) as order_count
FROM raw_orders
GROUP BY DATE(order_date), product_id;

BigQuery's incremental refresh only recomputes partitions where base table data changed. This works because operations like "SUM", "COUNT", "MIN", and "MAX" with "GROUP BY" are associative and commutative. You can merge partial results without rescanning everything. Window functions, "DISTINCT" on non-key columns, and non-deterministic User-Defined Functions (UDFs) break this contract and force full recomputation. The engine tracks modified partitions in base tables and propagates changes efficiently through the materialization graph.

Freshness as a Controllable Trade-off

Staleness isn't binary. You configure acceptable freshness SLAs based on business requirements. When data falls within the SLA window, queries automatically route to the materialized view for fast response. When freshness violations occur or real-time accuracy is critical, the query optimizer falls back to base tables, paying full compute costs for current data. This dial gives you explicit control: operational dashboards might tolerate 15-minute staleness for speed, while financial reports demand real-time accuracy and accept slower queries.

Applied Insight: When to Use Materialized Views

Materialized views deliver maximum value on append-heavy, time-partitioned fact tables where most changes land in recent partitions. This pattern minimizes refresh costs since only affected partitions recompute. Avoid them when base tables see frequent updates across many partitions or when queries need diverse aggregation patterns that don't align with a single materialization. Monitor staleness using "INFORMATION_SCHEMA.MATERIALIZED_VIEWS" and set up alerts when refresh lag exceeds your SLA. Always maintain a fallback path to query base tables directly if materialized view freshness becomes unreliable.