Dynamic Tables vs Materialized Views: When to Use What

The Core Difference

Both Dynamic Tables and Materialized Views pre-compute query results for performance. The critical difference is who controls refresh:

When to Use Dynamic Tables

Use Dynamic Tables when you need:

1. Complex transformations — multi-table joins, window functions, Python UDFs
2. Controlled freshness — e.g., reporting layer refreshed every 30 minutes
3. Pipeline chaining — building a bronze → silver → gold layer entirely in SQL

CREATE OR REPLACE DYNAMIC TABLE silver.orders_enriched
  TARGET_LAG = '15 minutes'
  WAREHOUSE = TRANSFORM_WH
AS
SELECT
  o.order_id,
  o.amount,
  c.name   AS customer_name,
  c.region AS customer_region,
  p.name   AS product_name
FROM raw.orders        o
JOIN raw.customers     c ON o.customer_id = c.id
JOIN raw.products      p ON o.product_id  = p.id;

When to Use Materialized Views

Use Materialized Views when you need:

1. Near-zero latency — results updated within seconds of source changes
2. Simple aggregations — COUNT, SUM, AVG on a single table
3. Query acceleration — MV auto-rewrites matching queries without any code change

CREATE OR REPLACE MATERIALIZED VIEW mv_daily_revenue AS
SELECT
  DATE_TRUNC('day', order_date) AS order_day,
  SUM(amount)                   AS total_revenue,
  COUNT(*)                      AS order_count
FROM raw.orders
GROUP BY 1;

Decision Framework

Need < 1 min freshness?
  YES → Materialized View (if simple agg) or Dynamic Table with lag='1 minute'
  NO  → Dynamic Table

Need multi-table joins?
  YES → Dynamic Table
  NO  → Either works

Building a medallion architecture?
  YES → Dynamic Tables (chain them: bronze → silver → gold)
  NO  → Either works

Conclusion

Dynamic Tables are the right choice for most modern data engineering workloads — they're more flexible and easier to chain. Materialized Views still shine for near-real-time, simple aggregations where Snowflake's automatic refresh is valuable.