Data Warehouse Optimizer

Data Warehouse Optimizer

Optimize query performance and resource utilization in Snowflake, BigQuery, and Redshift through clustering, partitioning, materialized views, and query profiling.

Activation Triggers

Activate on: "Snowflake optimization", "BigQuery performance", "Redshift tuning", "query optimization", "clustering key", "partitioning", "materialized view", "warehouse sizing", "query profile", "slow query"

NOT for: dbt project structure → dbt-analytics-engineer | Dimensional modeling → dimensional-modeler | Cost optimization beyond warehouse → data-cost-optimizer

Quick Start

1. Profile slow queries — use QUERY_PROFILE (Snowflake), INFORMATION_SCHEMA.JOBS (BigQuery), STL tables (Redshift)
2. Partition large tables — by date column (most common), reducing scan size by 10-100x
3. Add clustering — co-locate frequently filtered/joined columns within partitions
4. Materialize expensive aggregations — materialized views for dashboards, pre-aggregated metrics
5. Right-size warehouses — auto-suspend idle, auto-scale for concurrency, match size to workload

Core Capabilities

Architecture Patterns

Snowflake Clustering and Search Optimization

-- Cluster a large fact table by commonly filtered columns
ALTER TABLE fct_events
  CLUSTER BY (event_date, customer_id);

-- Verify clustering depth (lower = better, target < 2.0)
SELECT SYSTEM$CLUSTERING_INFORMATION('fct_events', '(event_date, customer_id)');

-- Search optimization for point lookups on high-cardinality columns
ALTER TABLE fct_events ADD SEARCH OPTIMIZATION
  ON EQUALITY(order_id), EQUALITY(email);

-- Result: range scans use clustering, point lookups use search optimization

BigQuery Partitioning + Clustering

-- Partition by date, cluster by high-cardinality filter columns
CREATE TABLE `project.dataset.fct_events`
PARTITION BY DATE(event_timestamp)
CLUSTER BY customer_id, event_type
AS
SELECT * FROM `project.dataset.raw_events`;

-- Query benefits: partition pruning + cluster pruning
-- Only scans partitions matching WHERE clause
SELECT customer_id, COUNT(*)
FROM `project.dataset.fct_events`
WHERE event_timestamp BETWEEN '2026-01-01' AND '2026-01-31'
  AND event_type = 'purchase'
GROUP BY customer_id;

-- Check bytes scanned reduction
-- Target: 90%+ reduction vs unpartitioned table

Warehouse Sizing Strategy (Snowflake)

Workload Type          Recommended Size     Auto-Suspend    Concurrency
─────────────          ────────────────     ────────────    ───────────
Dashboard queries      X-Small/Small        60s             Auto-scale (max 3)
Analyst ad-hoc         Medium               300s            1 cluster
dbt daily build        Large                Immediate       1 cluster
Data science / ML      X-Large+             Immediate       1 cluster

Key: separate workloads into different warehouses
     to prevent resource contention and enable per-workload billing

Anti-Patterns

1. Scanning full tables — always partition by date; a full scan of a 1TB table costs 10-50x more than a pruned scan
2. Too many clustering keys — 2-4 keys maximum; more keys reduce clustering effectiveness
3. Oversized warehouses — bigger does not always mean faster; profile first, right-size second
4. Ignoring spill-to-disk — queries spilling to remote storage are 10-100x slower; increase warehouse size or optimize query
5. Materializing volatile data — materialized views on rapidly changing tables cause constant refresh overhead

Quality Checklist

• Large tables (>1B rows) partitioned by date column
• Clustering keys set on top 2-3 filter/join columns
• Query profile reviewed for top 10 slowest queries monthly
• Spill-to-disk queries identified and optimized (increase size or rewrite)
• Materialized views created for expensive dashboard aggregations
• Warehouses auto-suspended when idle (60-300s)
• Workloads separated into dedicated warehouses
• Result cache hit rate >50% for repeated analytical queries
• Bytes scanned tracked and reduced quarter-over-quarter
• Unused tables/views identified and dropped quarterly