Building an Enterprise Social Data Lake: Ingesting Raw Social Telemetry into Snowflake and BigQuery
Why aggregated native analytics dashboards fail modern enterprise data teams, and how to build custom SQL attribution models on raw social event streams.
The Limitations of Walled Garden Dashboards
Meta Business Suite, TikTok Ads Manager, and LinkedIn Campaign Manager are designed to present their own platform in the most flattering light. Each platform claims credit for the same conversion using 7-day click and 1-day view attribution windows, resulting in overlapping, inaccurate reporting.
Enterprise marketing organizations bypass vendor dashboards by centralizing raw social telemetry into enterprise data lakes powered by Snowflake, Google BigQuery, and Databricks.
Architecture of a Modern Social Telemetry Warehouse
[Social Graph APIs] ──> [Airbyte / Fivetran Ingestion] ──> [Raw Staging Schema] ──> [dbt Dimensional Models] ──> [BI & Custom Attribution]
- Granular Event Streaming: Ingesting raw comment sentiment vectors, second-by-second video completion percentiles, and creator campaign webhook payloads.
- Identity Stitching: Connecting social handle identities with warehouse CRM accounts (Salesforce, HubSpot) using probabilistic and deterministic identity graphs.
- Custom Markov Chain Attribution: Calculating algorithmic Shapley and Markov values across all touchpoints to measure true incremental lift.
SocialHive Editorial
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