Attribution workloads have an unusual query profile. A single attribution run is not one complex query; it is dozens to hundreds of medium-complexity queries, each scanning the same event table across a different dimension or dimension combination, aggregating by group, and computing a contribution score. The engines that excel at single-query OLAP performance do not always excel at this fan-out pattern, and the cost structures are different enough that the "right" warehouse for your attribution workload depends heavily on which warehouse you are already paying for.
This post covers how Redshift, BigQuery, and Snowflake each handle the specific query patterns that attribution analysis requires. We are not going to award a winner; we are going to describe the mechanics so you can reason about your own situation.
What Attribution Queries Look Like
A typical attribution run against an event table with 500 million rows and 40 dimension columns generates roughly 40 to 60 separate queries depending on how the dimension tree is structured. Each query follows this general shape:
SELECT
dim_country,
SUM(event_value) AS total_value,
COUNT(DISTINCT user_id) AS unique_users
FROM fact_events
WHERE event_date BETWEEN '2025-10-01' AND '2025-10-31'
AND event_type = 'purchase'
GROUP BY dim_country;Run this once per dimension. Aggregation grain is one row per dimension value. The result feeds into a contribution scoring step that is compute-light. The expensive part is the repeated full or near-full table scan, aggregated across different grouping keys each time.
Several optimizations are possible on the query side: clustering the table on frequently used filter dimensions, caching intermediate aggregations per event type, and batching dimensions that have low cardinality into a single GROUPING SETS query. But even with these, the core pattern is high-volume repeated scans with varying grouping keys.
Amazon Redshift: Distribution Keys and Scan Cost
Redshift is a columnar, MPP (massively parallel processing) database where query performance is tightly coupled to how the table is physically distributed across nodes. A table with a well-chosen distribution key pushes filter-relevant data to the slice doing the work; a poorly chosen or even-distributed table forces full redistributes during aggregation.
For attribution queries on a fact table, the distribution choice matters a lot. If you distribute on event_date or event_type and your attribution filters by both, most queries will be node-local. If you have an ALL distribution on a small dimension table that gets joined for each query, Redshift can broadcast that table efficiently. The risk is that attribution queries touch many dimensions, some of which will not align with your chosen distribution key, triggering redistribution steps that appear in EXPLAIN output as DS_BCAST or DS_DIST operations.
Redshift's pricing model is provisioned compute: you pay for the cluster whether queries are running or not. For an attribution workload that runs continuously throughout the day, this can be cost-efficient. For a batch workload that runs once or twice daily, you are paying for idle compute between runs. Redshift Serverless changes this somewhat, but the per-query RPU cost structure can be expensive for the scan-heavy attribution pattern unless you tune concurrency scaling carefully.
One practical advantage: Redshift's materialized views with auto-refresh can pre-aggregate commonly filtered event subsets. If 80% of your attribution queries filter on the same event_type values, a materialized view over those events with incremental refresh reduces scan volume significantly. We have seen this cut attribution query runtime by 40 to 60% on Redshift setups where the filter conditions are stable.
BigQuery: Columnar on Object Storage and Cost Structure
BigQuery's architecture is meaningfully different from Redshift's. Compute and storage are separated; Dremel (the query engine) reads from Colossus (distributed columnar storage) on demand. There are no distribution keys because there is no static distribution. Instead, BigQuery uses clustering and partitioning as hints to the query planner about which files to read.
For attribution workloads, BigQuery's on-demand pricing model is either a significant advantage or a significant risk depending on your query volume. On-demand pricing charges per byte scanned. An attribution run that scans 500 million rows at 200 bytes per row is scanning 100 GB per dimension query. At 40 dimension queries per run, that is 4 TB per attribution pass at on-demand pricing, which adds up quickly on multiple runs per day. BigQuery's flat-rate slots pricing makes more sense for consistent, high-volume workloads.
BigQuery handles the fan-out pattern well because its scheduler can run concurrent queries efficiently across Dremel workers without contention on locks or buffer pools. On a well-partitioned table, queries that do not share partition ranges barely interact. This makes it straightforward to parallelize attribution dimension scans across concurrent slots rather than serializing them.
-- BigQuery: GROUPING SETS to batch low-cardinality dimensions
SELECT
dim_country,
dim_device_type,
dim_channel,
GROUPING(dim_country, dim_device_type, dim_channel) AS grouping_id,
SUM(event_value) AS total_value
FROM `project.dataset.fact_events`
WHERE event_date BETWEEN '2025-10-01' AND '2025-10-31'
AND event_type = 'purchase'
GROUP BY GROUPING SETS (
(dim_country),
(dim_device_type),
(dim_channel)
);BigQuery executes GROUPING SETS as a single pass with multiple aggregation paths, which reduces the number of full table scans from three to one for that query. For the attribution pattern, this is a meaningful optimization. Redshift and Snowflake both support GROUPING SETS, but the efficiency gain varies by warehouse and table size.
Snowflake: Virtual Warehouses and Automatic Clustering
Snowflake separates compute (virtual warehouses) from storage (S3-backed micro-partitions) similarly to BigQuery, but with an explicit compute unit you start and stop manually or via auto-suspend. Each virtual warehouse has its own local SSD cache (the "data cache") that stores recently accessed micro-partitions. For attribution workloads that repeatedly scan the same event table across different dimensions, this cache becomes critical.
On the first attribution run after a cold warehouse start, Snowflake reads micro-partitions from remote storage. On subsequent runs, the hot micro-partitions are in local SSD. An attribution workload running multiple times per day against the same event table can see the second run execute 3 to 5x faster than the first purely because of cache hits, assuming the virtual warehouse has not auto-suspended between runs.
Snowflake's automatic clustering (available for Snowflake-managed clustering) organizes micro-partitions by a chosen column, allowing the query planner to skip micro-partitions that do not match a filter. For an event table clustered on event_date, attribution queries with a date range filter prune aggressively. Without clustering, Snowflake reads all micro-partitions regardless of filter predicates, which is expensive at scale.
One consideration for attribution specifically: Snowflake's credit consumption scales with query complexity and data volume, not just wall time. A fan-out workload running 50 concurrent queries on a large warehouse may consume significantly more credits per run than a serialized workload on a smaller warehouse. Sizing the virtual warehouse correctly for your concurrency needs requires some empirical testing; the optimal size for 10 concurrent queries is not the same as for 1 sequential query, even at the same total data scanned.
Practical Implications for Attribution Workload Design
The warehouse engine should inform your query design, not override it, but there are meaningful tactical choices.
On Redshift, invest in clustering and sort keys on the dimensions you filter most frequently. Materialized views with incremental refresh on high-frequency event type subsets pay off consistently. Monitor for redistribute steps in explain output and restructure queries that trigger them repeatedly.
On BigQuery, use partitioning on the date dimension aggressively and apply GROUPING SETS to batch dimension scans that share the same filter conditions. If you are on flat-rate slots, attribution fan-out workloads are well-suited to slot-based pricing. On on-demand, track bytes-billed per attribution run closely and set project-level byte limits as a cost guardrail during development.
On Snowflake, manage virtual warehouse auto-suspend carefully. A 5-minute auto-suspend interval is appropriate for interactive query workloads; for an attribution batch that runs every 2 hours, you lose the data cache benefit if the warehouse suspends between runs. For a large event table, keeping the warehouse warm between attribution runs can reduce average run time substantially. Test both approaches with your actual data volume before committing to a configuration.
Which Warehouse Should You Use for Attribution
We are not in a position to tell you to switch warehouses for attribution. The cost of migration and the disruption to existing workflows almost always outweighs any performance advantage for a product at this stage. The right answer is to tune your attribution queries for the warehouse you already have, understand the pricing implications of the scan-heavy pattern, and monitor cost-per-attribution-run as a metric you track rather than discover after the bill arrives.
If you are making a new warehouse selection and attribution workloads will be a primary use case, the query pattern described above maps well to BigQuery's architecture at scale, but Snowflake's local cache behavior makes it competitive if your workload fits within a well-managed virtual warehouse lifecycle. Redshift is the right call if you are already deeply invested in the AWS ecosystem and can tune distribution keys for your specific event table structure.
The meaningful differentiator across all three is not peak query performance; it is how the pricing model scales with a high-frequency scan-and-aggregate workload. Build a cost model for your expected attribution run frequency before you commit to any of them at production scale.