Cohort membership, signal snapshots, and event-level data delivered as partitioned Parquet to Snowflake, BigQuery, Redshift, or Databricks. Your data team treats Flockr as another source — model it, join it, query it, push it back out through reverse-ETL.
Data warehouses are where your team does the analysis that doesn't fit a vendor-built dashboard. Custom segmentation models. Attribution analysis. Predictive scoring. Internal reporting at whatever granularity your business runs on.
Flockr's warehouse integration produces the data Flockr would never expose elsewhere — full cohort membership timelines, product-level signal snapshots, raw event streams — in the place your data team already works.
These patterns describe what your data team does with Flockr once it's in the warehouse — not how Flockr delivers it. The framing matches how data teams already think about warehouse data.
Join Flockr's cohort and signal data with the rest of your warehouse — order history, customer records, marketing-spend data, anything else your team has loaded. Build the analytical models that need cross-source context: lifetime value by cohort, signal-attribution analysis, custom audience scoring.
flockr_attributed_purchase events with the customer's own order and marketing-spend tables.The warehouse becomes an activation hub through reverse-ETL. Hightouch, Census, Polytomic, or your own pipeline reads from the warehouse and pushes to destinations — ad platforms, customer engagement platforms, your own internal tools. Cohort definitions built on Flockr data flow out to wherever the customer's existing reverse-ETL routes them.
scarcity_responsive_shopper cohort AND the customer's own "high-CLV prediction" segment, syncing to Meta Custom Audiences.Internal dashboards and operational reporting where Flockr data is one input among several. Looker, Mode, Hex, Power BI, Tableau — whatever the customer's BI team runs. Demand-state-attributable revenue. Cohort performance over time. Signal-family impact. The reports that need Flockr context but live in the customer's own reporting stack.
The warehouse integration is the most standards-driven in the Flockr platform. Parquet on object storage, ingested through whatever native pattern the customer's warehouse supports. No proprietary protocols, no custom connectors, no warehouse-specific code on Flockr's side.
Flockr writes partitioned Parquet files to a Flockr-managed S3 bucket on a configurable schedule — typically hourly for cohort membership, daily for signal snapshots, real-time append for event-level data. Files are partitioned by client and date for efficient query pruning. The schema is documented and versioned; new fields appear additively without breaking existing queries.
The customer's warehouse picks up the files through its native ingestion pattern. No third-party ETL tooling required — though customers running Fivetran or Airbyte can also wire those in if it fits their stack.
The export shape is identical across warehouses — only the customer's native ingestion changes. Flockr's responsibility ends at the Parquet write; the customer's data team owns the ingestion path that fits their warehouse.
Four warehouses, all peers in how they're used. The Parquet-on-S3 export pattern is identical for each — only the customer's native ingestion differs.
The most common warehouse in retail data stacks. Flockr's Parquet exports are picked up via Snowpipe for continuous ingestion, or via external tables for query-on-read patterns. Particularly well-suited to retailers running Snowflake's Cortex AI features alongside Flockr cohort data for predictive modelling.
Google-stack data infrastructure. Flockr's exports land via BigQuery Data Transfer Service or as external tables on GCS-mirrored S3 buckets. Strong fit for retailers running GA4 export in the same warehouse — Flockr cohort data joins naturally with GA4's event tables.
AWS-stack data infrastructure. Flockr's S3-resident Parquet is the native shape for Redshift ingestion via COPY commands or Redshift Spectrum for in-place querying. The schema is documented, the export pattern follows AWS conventions.
Data-engineering and data-science-heavy retailers. Flockr's Parquet exports work directly with Databricks Auto Loader for streaming ingestion, or with Delta Live Tables for the customer's own transformation pipelines.
These tools read directly from the warehouse and push to destinations. Once Flockr data is in the warehouse, any of these tools can sync it back out to ad platforms, customer engagement platforms, or any of the destinations they support. The warehouse becomes an activation hub for destinations Flockr doesn't connect to directly.
Don't see your warehouse? The Parquet-on-S3 export pattern works with any warehouse that supports external tables or standard ingestion of object-storage data.Contact us about your stack →
A retailer's data team configures Flockr's Snowflake integration. Cohort membership, signal snapshots, and event-level data flow into a flockr schema in their warehouse, partitioned by date. Three teams pick it up.
One warehouse export configured once. Three teams use the same data three different ways — none requires Flockr-specific tooling, because the data team's existing tools treat Flockr as another source.
Flockr's warehouse integration delivers three datasets: cohort_membership (which visitors are in which cohorts over time), signal_snapshots (product-level signal state, daily), and events (raw Flockr-captured events for custom modelling). Each team uses different combinations.
The analytics team builds a revenue-attribution model joining cohort_membership with the customer's own orders table. The model answers "what percentage of revenue in Q4 came from visitors who entered a Flockr-defined cohort during the same period?" — a question Flockr's own dashboards can't answer because it requires the customer's order data.
The data science team trains a predictive model on signal_snapshots and events to forecast which visitors are likely to enter the scarcity_tipped_purchaser cohort. The output feeds an on-site personalisation system that pre-emptively shows scarcity messaging to high-probability visitors.
The marketing operations team configures a Hightouch sync that selects visitors in Flockr's scarcity_responsive_shopper cohort AND the customer's own "predicted-CLV top 20%" segment. The combined audience pushes to Meta Custom Audiences as a Customer Match list — a tighter audience than either source could produce alone.
One warehouse export. Three teams. Three downstream workloads. None requires Flockr-specific configuration once the data is in the warehouse — the data team's existing tools treat Flockr as another source.
Some retailers don't run a traditional event-based CDP — instead, they use the warehouse plus reverse-ETL as their effective customer data infrastructure. Flockr data lands in the warehouse, gets joined and modelled there, and flows out to destinations through Hightouch, Census, Polytomic, or equivalent. The operational model is different from a Segment-style CDP, but the effective capability is similar: a unified data view feeding multiple downstream activations.
Flockr supports both patterns equally well. The warehouse integration is the same in either case — what changes is what the customer puts in front of it. Customers running both a traditional CDP and a warehouse-plus-reverse-ETL stack can use Flockr for different cohort types on each path, with the warehouse handling the modelling-heavy cohorts and the CDP handling the real-time triggers.
See the customer data platforms sub-page →Thirty minutes with a Flockr engineer. We'll set up the Parquet export, confirm the ingestion pattern that fits your warehouse, and show what queries your data team can run once Flockr's another source on the shelf.