Meiro Pipes Integration
Connect Heap and BigQuery.
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 BigQuery, resolves customer identity across both, and pushes enriched data back — without Heap Connect's enterprise tier or a stack of middleware.
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Two teams. Same broken pipe.
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 BigQuery — 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
Why connecting Heap and BigQuery takes three tools when it should take one
Native connectors between Heap and BigQuery 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 BigQuery 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 BigQuery, and the unified profile syncs back as a single user in Heap.
One platform. Collect, resolve, model, activate.
Collect
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.
Load & Model
Events land in your BigQuery warehouse automatically. Pipes connects directly — browse tables, map columns, model data. Your warehouse stays your source of truth.
Resolve Identity
Pipes stitches user profiles across Heap events and BigQuery 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.
Activate
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.
Use case: Lead scoring with warehouse data, activated in Heap
- 1 Heap tracks product events — signups, feature adoption, engagement patterns.
- 2 Pipes loads those events into BigQuery alongside your CRM data — deal stage, contract value, company size.
- 3 In BigQuery, your team builds a lead score model combining product usage signals with commercial data.
- 4 Pipes resolves identity so the same user is recognized across both systems — even when Heap has an anonymous cookie and BigQuery has an email from Salesforce.
- 5 The scored profiles are pushed back into Heap as enriched user properties. Your growth team builds segments on "high product engagement + enterprise deal stage" without writing SQL or filing a single engineering ticket.
Time from setup to first enriched cohort in Heap: under a day.
The pain is real
The way we set up Heap with auto capture results in a lot of overhead to sift through many events.— Heap user, G2
ETL tools often run into problems with the ever-changing nature of customer behavioral data, making this a sticking point where single source of truth initiatives break down.— Data engineering community, 2024
Under the hood
Heap Connector
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.
BigQuery Connector
Direct BigQuery connection via service account credentials. Browse datasets and tables, inspect columns, map identifier columns to Meiro identity types. Handles BigQuery's nested and repeated field structures natively.
Identity Resolution
Deterministic stitching across identifier types: email, user_id, device_id, cookie. Configurable merge limits (maxIdentifiers) and priority hierarchy prevent false merges. No probabilistic matching.
Reverse ETL / Profile Sync
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.
Transform Layer
Sandboxed JavaScript functions for event transformation, filtering, and enrichment. Run inline — no external orchestrator needed.
Self-Hosted Option
Deploy on your own infrastructure for full data sovereignty. Or use Meiro Cloud. Your data never leaves your perimeter unless you want it to.
Live in minutes, not months
Connect Heap
Add Heap as a Source via its export API or warehouse connector. Events start landing in your pipeline.
Connect BigQuery
Add your BigQuery credentials. Browse tables, map identifiers, start modeling.
Resolve & Activate
Pipes stitches identity across both systems. Push enriched profiles back to Heap or anywhere in your stack.
Stop duct-taping your data stack.
Connect Heap and BigQuery in one platform. Resolve identity. Push enriched data back. Start free.