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Analytics & warehouse integrations

Export Active Reach events to your data warehouse for custom analytics, ML training, or compliance archival. Import from Segment for unified event collection.

BigQuery (export)

Setup

  1. Go to Settings → Data Pipes → New → BigQuery
  2. Upload a Google Cloud Service Account JSON key with BigQuery write permissions
  3. Enter the destination: project ID, dataset, and table name
  4. Select which events to export: all, or filter by event type
  5. Activate

How it works

Events stream via Kafka to a dedicated BigQuery writer. New events appear in your table within minutes. The schema auto-evolves — new event properties create new columns.

Schema

Each row in BigQuery represents one event:

ColumnTypeDescription
event_idSTRINGUnique event identifier
event_typeSTRINGtrack, identify, page, group, alias
event_nameSTRINGCustom event name (for track events)
user_idSTRINGIdentified user ID
anonymous_idSTRINGAnonymous session ID
timestampTIMESTAMPWhen the event occurred
propertiesJSONEvent properties (nested)
contextJSONDevice, browser, location context

Snowflake (export)

Setup

  1. Go to Settings → Data Pipes → New → Snowflake
  2. Enter Snowflake account, warehouse, database, schema, and table
  3. Authenticate via OAuth or username/password
  4. Select events to export and activate

Same streaming behavior as BigQuery — near-real-time, auto-evolving schema.

Databricks (export)

Setup

  1. Go to Settings → Data Pipes → New → Databricks
  2. Enter workspace URL and OAuth token
  3. Configure the destination catalog, schema, and table
  4. Activate

Segment (import)

Setup

  1. Go to Settings → Data Pipes → New → Segment
  2. Enter your Segment workspace token
  3. Active Reach appears as a destination in your Segment workspace
  4. Events from any Segment source automatically flow into Active Reach

What syncs

All Segment event types (track, identify, page, group, alias) map directly to Active Reach’s event schema — no transformation needed.

What’s next

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