Amazon Redshift

Datafly Signal writes events directly into a Redshift table so you can analyse first-party behaviour alongside the rest of your AWS data estate.

Why route Amazon Redshift through Datafly Signal

• The data lands in infrastructure you own. Signal writes to your Amazon Redshift 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 and last name, full name and postcode are normalised (trim and lowercase) and hashed with SHA-256 inside your own deployment, so the table holds email_sha256, phone_sha256 and the other digests rather than raw personal data. A raw column is an opt-in edit to the installed blueprint.
• 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.
• Presets for Retail, Insurance, Travel, Media & Streaming, Publishing, 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, and numeric properties typed as numbers. Every other event still lands with its properties.
• One consent decision governs every destination. Amazon Redshift is gated by the same consent state as the rest of your stack, so a withdrawal applies everywhere at once.

Prerequisites

Before configuring Amazon Redshift in Signal, you need an AWS account with a Redshift cluster, a target schema and table, and a database user with INSERT privileges.

CREATE SCHEMA datafly;
 
CREATE TABLE datafly.events (
  message_id      VARCHAR(64),
  event_type      VARCHAR(20),
  event_name      VARCHAR(256),
  anonymous_id    VARCHAR(64),
  user_id         VARCHAR(256),
  ip_address      VARCHAR(64),
  user_agent      VARCHAR(1024),
  page_url        VARCHAR(4096),
  page_path       VARCHAR(2048),
  page_referrer   VARCHAR(4096),
  org_id          VARCHAR(64),
  pipeline_id     VARCHAR(64),
  event_timestamp TIMESTAMP,
  received_at     TIMESTAMP,
  properties_json VARCHAR(65535),
  context_json    VARCHAR(65535)
)
DISTKEY(anonymous_id)
SORTKEY(event_timestamp);

CREATE USER datafly_signal PASSWORD 'your_secure_password';
GRANT USAGE ON SCHEMA datafly TO datafly_signal;
GRANT INSERT ON TABLE datafly.events TO datafly_signal;

Configuration

Signal Setup

Quick Setup

1. Navigate to Integrations in the sidebar.
2. Open the Integration Library tab.
3. Find Amazon Redshift or filter by Warehouse.
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/amazon_redshift/install \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Amazon Redshift",
    "variant": "default",
    "config": {
      "host": "datafly-analytics.abc123.us-east-1.redshift.amazonaws.com",
      "port": "5439",
      "database": "analytics",
      "schema_name": "datafly",
      "table_name": "events",
      "username": "datafly_signal",
      "password": "your_secure_password"
    },
    "delivery_mode": "server_side"
  }'

Schema

Signal inserts one row per event over these columns:

Where mapped fields land

The table layout is fixed: the envelope columns above plus properties_json. The blueprint shapes properties_json: event fields from the preset (for example transaction_id, value, insurance_type, policy_term_months) sit at its top level, and the preset’s envelope block (identity digests, UTM, consent flags) sits under @root:

SELECT event_timestamp,
       JSON_EXTRACT_PATH_TEXT(properties_json, 'transaction_id')        AS policy_number,
       JSON_EXTRACT_PATH_TEXT(properties_json, 'value')::FLOAT          AS premium,
       JSON_EXTRACT_PATH_TEXT(properties_json, '@root', 'email_sha256') AS email_sha256
FROM datafly.events
WHERE event_name = 'purchase';

Signal does not write mapped fields to their own typed columns yet. Create views over properties_json for the columns you query often.

Stored event context

Alongside the fields your blueprint maps (where email, phone, names and address are stored as normalised SHA-256 by default), each row 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 written to the ip_address, user_agent, page_url, page_path and page_referrer columns and, in full, to the context_json column, 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

Redshift is a first-party destination under your control. The default blueprint forwards all events. Apply consent filtering inside your pipeline transforms or via downstream materialised views on the preset’s consent flags (JSON_EXTRACT_PATH_TEXT(properties_json, '@root', 'consent_analytics')) or context_json if needed.

Testing

1. Enable the integration in Signal and trigger a test event on your website.
2. Connect to the Redshift cluster and query the target table:

SELECT * FROM datafly.events ORDER BY event_timestamp DESC LIMIT 10;

3. Verify event rows are appearing with correct data.
4. In Signal, check the Live Events view to confirm delivery status shows as successful.

Troubleshooting

See also

• Data Warehouses overview
• Snowflake
• Google BigQuery
• Amazon S3