MongoDB

Datafly Signal inserts first-party events as documents into a MongoDB collection — ideal for schema-agnostic NoSQL storage with rich querying, aggregation, and indexing.

Why route MongoDB through Datafly Signal

• The data lands in infrastructure you own. Signal writes to your MongoDB directly from your own deployment. Datafly operates no intermediary and never holds a copy of your event data.
• Personal data is hashed on the way in. Email, phone (shaped to E.164 first), first, last and full name and postcode are normalised (trimmed and lower-cased) and hashed with SHA-256 inside your own deployment, so the collection holds email_sha256, phone_sha256 and the other digests rather than the raw values. Storing a raw value is a deliberate edit to the installed blueprint.
• Only what the blueprint maps is stored. The properties Signal writes are exactly the fields the blueprint maps. Identify traits and client properties the blueprint does not map are never written, so a stray email in an event property cannot land in MongoDB in plain text.
• Presets for Retail, Insurance, Travel, Media & Streaming, B2B / SaaS, Automotive and General. Each vertical preset names one field per property in the matching event spec, with canonical names (transaction_id, value, items), legacy aliases (order_id, revenue, products) read automatically, numbers typed as numbers and currency codes upper-cased.
• Client IP and user agent are recorded as event context. Both come from the original visitor request rather than from your server, so geography and device analysis done in the warehouse reflects the actual visitor.
• One consent decision governs every destination. MongoDB is gated by the same consent state as the rest of your stack, so a withdrawal applies everywhere at once.

Prerequisites

Before configuring MongoDB in Signal, you need a MongoDB instance (self-hosted or Atlas), a database and collection, and a connection URI.

use admin
db.createUser({
  user: "datafly_signal",
  pwd: "your_secure_password",
  roles: [
    { role: "readWrite", db: "datafly_events" }
  ]
})

use datafly_events
db.createCollection("events")
db.events.createIndex({ "timestamp": -1 })
db.events.createIndex({ "event_name": 1, "timestamp": -1 })

Configuration

Signal Setup

Quick Setup

1. Navigate to Integrations in the sidebar.
2. Open the Integration Library tab.
3. Find MongoDB or filter by Database.
4. Click Install, select a variant if available, and fill in the required fields.
5. Click Install Integration to create the integration with a ready-to-use default blueprint.

API Setup

curl -X POST http://localhost:8084/v1/admin/integration-catalog/mongodb/install \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "MongoDB",
    "variant": "default",
    "config": {
      "uri": "mongodb+srv://datafly_signal:your_password@cluster0.abc123.mongodb.net/datafly_events",
      "database": "datafly_events",
      "collection": "events"
    },
    "delivery_mode": "server_side"
  }'

Schema

Each event is inserted as one document with the message_id as _id (write concern majority); a redelivered event hits the duplicate key and is treated as already delivered:

{
  _id: "<message_id>",
  event_type: "track",
  event_name: "purchase",
  anonymous_id: "anon-7f3c",
  user_id: "user-1042",
  timestamp: ISODate("2026-09-23T10:00:00Z"),
  received_at: ISODate("2026-09-23T10:00:00Z"),
  org_id: "...",
  pipeline_id: "...",
  properties: {
    "@root": { email_sha256: "86e0b9e5...", event_name: "purchase", utm_source: "google" },
    transaction_id: "T-1001",
    value: 59.98,
    currency: "GBP",
    items: [{ item_id: "SKU-1", item_name: "Shoe", price: 29.99, quantity: 2 }]
  },
  context: { ip: "203.0.113.9", user_agent: "Mozilla/5.0", page: { url: "https://shop.example/checkout", ... }, ... },
  collected_ids: null,
  click_ids: null
}

user_id is an empty string for an anonymous visitor. The context sub-document uses the event’s JSON field names (user_agent, vendor_ids, accept_language), the same as the other document stores, and leaves out empty fields. Documents written before this change carry the older lower-cased names (useragent, vendorids, acceptlanguage) and every empty field; a query or index on those names should read both across the change (for example with $ifNull). Documents cannot exceed 16 MiB.

What the properties hold

The blueprint decides what properties contains, and only mapped fields are written (identify traits and unmapped client properties never are):

• The event’s own fields from the preset sit at the top level, for example transaction_id, value, currency, items, insurance_type or policy_term_months.
• The preset’s global block sits under @root: copies of the envelope (event_name, event_type, user_id, anonymous_id, event_id, message_id, event_timestamp, page_url, page_referrer, ip_address, user_agent, locale, timezone, source_id), utm_source, utm_medium, utm_campaign, the consent flags consent_analytics and consent_marketing, and the hashed identity email_sha256, phone_sha256, first_name_sha256, last_name_sha256, name_sha256.
• identify adds name_sha256 and postal_code_sha256 at the top level.
• An event the preset does not list stores its envelope and the @root block only.

For example:

db.events.find(
  { event_name: "purchase" },
  { timestamp: 1, "properties.transaction_id": 1, "properties.value": 1, "properties.@root.email_sha256": 1 }
).sort({ timestamp: -1 }).limit(10)

Mapped fields stay inside properties; they are not lifted to top-level fields. What else a row carries outside the blueprint (the whole event context) is under review in #1414.

Stored event context

Alongside the fields your blueprint maps (where email, phone, names and address are stored as normalised SHA-256 by default), each document carries the event’s collected context exactly as Signal received it. The blueprint does not filter or hash the context: this is deliberate (decision 2026-09-23), so the context is complete for attribution, fraud and debugging queries, and it is listed here so you can decide what to keep.

They are stored as the document’s context field (Bigtable: the context column), using the event context’s JSON field names, beside anonymous_id and user_id. Click and collected IDs outside the context are stored only where the blueprint maps them.

The IP address, device and advertising IDs, and cookie and click identifiers are personal data under UK and EU GDPR. To keep any of them out of storage, strip, mask or hash the field for every integration with the organisation data layer, apply URL redaction to page URLs, or remove it in the installed blueprint; and set the storage’s retention to match your privacy notice. See Personal data by integration.

Consent

MongoDB is a first-party destination under your control, gated by the same consent state as every other destination. Each stored event also records the visitor’s consent at collection time: the preset writes consent_analytics and consent_marketing under @root in the properties (properties.@root), and the full consent object is in the context. Use them in views or queries if your governance needs a per-row consent filter.

Testing

1. Enable the integration in Signal and trigger a test event on your website.
2. Connect to your MongoDB instance and query the collection:

use datafly_events
db.events.find().sort({ timestamp: -1 }).limit(10)

3. Or in Atlas, use the Browse Collections interface to view documents.
4. In Signal, check the Live Events view to confirm delivery status shows as successful.

Troubleshooting

See also

• Google Firestore
• Azure Cosmos DB
• Amazon DynamoDB
• Elasticsearch