DatriseAI-first ETL

StackAdapt to Snowflake Integration

AI-first ETL from StackAdapt into Snowflake. Governed entities, incremental sync, typed landing tables.

How Datrise loads StackAdapt into Snowflake

Datrise syncs StackAdapt's campaigns, line items, creatives, spend, impressions, and conversions into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

StackAdapt: Programmatic advertising across native and display.

Snowflake: Cloud data warehouse with separated compute and storage.

How StackAdapt entities map to Snowflake

StackAdapt entitySnowflake objectNotes
campaignsstackadapt_campaignsid PK · custom fields → VARIANT columns
line itemsstackadapt_line_itemsid PK · linked to stackadapt_campaigns
creativesstackadapt_creativesid PK · linked to stackadapt_campaigns
spendstackadapt_spendid PK · linked to stackadapt_campaigns

FAQ

How does Datrise handle StackAdapt's custom fields in Snowflake?

Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.

How does the StackAdapt to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

Early access

Connect StackAdapt to Snowflake the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.