Meiro Pipes Integration
Meiro Pipes sits in the middle of that loop — resolving identity across behavioral events, warehouse records, and every other source before data moves in either direction.
Meiro Pipes syncs Heap events into Snowflake, resolves customer identity across both systems, and pushes enriched data back — without needing Heap Connect's Premier tier or a reverse ETL vendor.
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Heap captures what users do in your product. Your warehouse has everything else — deal stage from the CRM, billing tier from the product DB, support history. You need that commercial context in Heap to build segments that mean something: not just "users who clicked feature X" but "users who clicked feature X and are on a growth plan with an open renewal."
The data is there. The problem is that Heap knows users by device_id and anonymous cookie. Your CRM knows them by email. Your product DB knows them by account_id. When enriched properties sync back from the warehouse, they land on the wrong profile, create duplicates, or partially match. Your cohorts look complete and aren't.
You've got Heap exporting events to Snowflake — native connector, works fine. You've got CRM data, product DB records, and billing history landing in the same warehouse. The enrichment model is built. The problem is the last step: getting enriched user properties back into Heap correctly.
The reverse sync fails on identity. Heap has a user identified by device_id. The enriched record in the warehouse is keyed on email. No connector resolves that gap automatically — so you write a join, it works in staging, and it silently breaks in production when a user has three devices and two email addresses. You're the one debugging it at 11pm.
The Real Problem
Native connectors between Heap and Snowflake handle the data movement. Heap's export lands behavioral events in the warehouse. A reverse ETL connector pushes warehouse data back as user properties. Both directions work at the plumbing level.
The gap is identity. Heap tracks users from first anonymous session through authentication — building its own internal identity graph anchored on device_id, then resolving to user_id on login. Your warehouse has those same users keyed differently: email in the CRM table, account_id in the product DB, customer_id in billing. When you run the enrichment model in Snowflake and push results back to Heap, the reverse ETL connector maps rows to users by whatever identifier you configure — one identifier, one mapping. It doesn't know that device_id: abc, email: [email protected], and account_id: 789 are the same person unless something resolved that first.
When identity isn't resolved before the sync, enriched properties land on partial profiles. A user with three devices gets three partial records. A user who converted from anonymous to authenticated exists twice. The cohort you built on "enterprise plan + churned feature X" is a subset of the real answer — and you won't know how large a subset until you audit the data.
Pipes resolves identity across all identifier types — device_id, user_id, email, account_id, CRM ID — before data moves in either direction. The enrichment loop closes correctly: behavioral context from Heap joins commercial context from Snowflake, and the unified profile syncs back as a single user in Heap.
Pipes connects to Heap via its export API and warehouse connector. Events are ingested on a scheduled or near-real-time basis — no replacement of your existing Heap SDK or tracking plan required.
Events land in your Snowflake warehouse automatically. Pipes connects directly — browse tables, map columns, model data. Your warehouse stays your source of truth.
Pipes stitches user profiles across Heap events and Snowflake records using deterministic matching on email, user_id, device_id, or any identifier you define. Configurable merge limits prevent false matches on shared devices. No probabilistic guesswork.
Enriched profiles and segments flow back into Heap via scheduled or real-time sync. Your growth team gets warehouse-enriched cohorts directly in the tool they already use — no reverse ETL vendor required.
Time from setup to first enriched cohort in Heap: under a day.
The way we set up Heap with auto capture results in a lot of overhead to sift through many events. I would prefer if we had a specific plan of what we want to capture instead of everything.— Heap user, G2
The graphical and data analysis features that come out-of-the-box are fine, but not fancy. We also bought Heap Connect so we do have the capability to define arbitrarily complex queries, but it would be nice to have better capabilities out-of-the-box.— Heap user, G2
Connects to Heap via its export API and warehouse connector. Ingests events on a scheduled or near-real-time basis. Supports event filtering and transformation via Pipes sandbox functions. No replacement of your existing Heap SDK.
Direct Snowflake connection via warehouse credentials. Browse schemas and tables, inspect columns, map identifier columns to Meiro identity types. Native support — no staging bucket or intermediate layer required.
Deterministic stitching across identifier types: email, user_id, device_id, cookie. Configurable merge limits (maxIdentifiers) and priority hierarchy prevent false merges. No probabilistic matching.
Scheduled exports or real-time Live Profile Sync. Push enriched profiles and audience segments back to Heap or any downstream destination via custom send functions.
Sandboxed JavaScript functions for event transformation, filtering, and enrichment. Run inline — no external orchestrator needed.
Deploy on your own infrastructure for full data sovereignty. Or use Meiro Cloud. Your data never leaves your perimeter unless you want it to.
Add Heap as a Source via its export API or warehouse connector. Events start landing in your pipeline.
Add your Snowflake credentials. Browse tables, map identifiers, start modeling.
Pipes stitches identity across both systems. Push enriched profiles back to Heap or anywhere in your stack.
Connect Heap and Snowflake in one platform. Resolve identity. Push enriched data back. Start free.