Glossary
Warehouse-Native Analytics
Warehouse-native analytics is an architecture in which an analytics platform runs all of its computations directly inside the customer's existing cloud data warehouse (Snowflake, BigQuery, Redshift, or Postgres) rather than extracting and copying data to a separate, vendor-controlled store. The vendor's application queries your warehouse using the compute and storage you already own; your data never leaves.
How it differs from traditional analytics
The conventional model for SaaS analytics tools is extract, load, and transform: data leaves your systems, gets ingested into the vendor's proprietary store, and the vendor runs queries against their own copy. You lose visibility into what happens to your data in transit, you pay for duplicated storage, and every schema change in your warehouse requires a re-sync.
Warehouse-native analytics inverts this. The tool is granted read (and sometimes write) access to your warehouse through a service account or a native application framework like Snowflake Native Apps. All aggregations, joins, and model computations happen inside the warehouse. The tool's UI presents results; it does not store source data.
Why the architecture matters for attribution
Attribution models are computationally expensive and data-intensive. A full-funnel multi-touch model must join web events, CRM records, ad impression logs, and product usage data. In a copy-and-sync architecture, every one of those tables must be replicated to the vendor and kept in sync, which is slow, costly, and fragile when upstream schemas change.
With a warehouse-native design, the attribution engine queries the live tables directly. When your data engineering team adds a new event type or renames a column, it is available to the attribution model immediately with no re-ingestion step. This also means that raw-level data (individual ad impressions, server-side events, hashed email addresses) stays inside your security perimeter, which matters for GDPR, CCPA, and enterprise security reviews.
Key properties of a warehouse-native analytics system
Zero data copying
Source data stays in your warehouse. The analytics layer queries in place, so storage costs are not duplicated and there is no sync lag.
Your security perimeter
PII, raw event data, and hashed identifiers never leave your infrastructure. Role-based access controls in your warehouse apply automatically.
Live schema compatibility
Because queries run against the live warehouse, schema changes made by your data team are reflected immediately with no re-ingestion required.
Warehouse-native vs. reverse ETL: different layers
Warehouse-native analytics describes where the analytics computation runs: inside the warehouse. Reverse ETL describes a complementary process in which the results of those computations (segments, scores, enriched records) are pushed back out to operational tools like CRMs and ad platforms. The two concepts often appear together in a modern data stack, and they are part of the same architectural philosophy: the warehouse is the system of record, and data moves out from it only when a destination needs it.
How AttriByte implements warehouse-native analytics
AttriByte connects to Snowflake, BigQuery, Redshift, and Postgres using a read-only service account (write is required only for materializing attribution output tables). All six attribution models (first-touch, last-touch, linear, time-decay, U-shaped, and W-shaped) compute inside your warehouse. The Atlas AI analyst writes SQL against your warehouse at query time rather than against a cached extract. Audience segments built in AttriByte are materialized as tables or views in your warehouse before being synced to ad platforms via reverse ETL.
This architecture is the reason AttriByte can run multi-touch attribution at the account level across hundreds of millions of events without asking you to replicate your data lake to a third-party store.
Related glossary terms
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