Analytical Data Warehouse in Snowflake

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Omeda provides access to the Analytical Data Warehouse through a Snowflake Secure Data Share, allowing you to directly query and analyze your Omeda data within your own Snowflake environment. This eliminates the need for manual exports and provides convenient, reliable access to your warehouse data with nightly updates.

Use Cases:

Run queries using your existing Snowflake tools and infrastructure
Integrate Omeda data with other datasets in your Snowflake account
Build custom reports, dashboards, and advanced analytics workflows
Access fresh data with nightly updates

How it Works

Omeda provisions a read-only, Secure Data Share in Snowflake containing your Analytical Data Warehouse.

Once connected, you can query it directly in your Snowflake account, with updates automatically applied each night.

Data Available in the Share

Your Analytical Data Warehouse includes data grouped into categories for easier navigation:

Customer Data

Customer identifiers.
Names and profile attributes (first/last name, title, gender).
Lifecycle dates (record created date, original customer acquisition date) and import dates.
Original promo code captured at creation.
Postal addresses (company, street lines 1–3, city, region code, postal code, country code, status).
Email addresses, creation date, status code, invalid-email flag, and invalidation date.
Phone details (number, extension, status, phone type).

Demographic Data

Demographic attributes (name, value ID, optional alternate ID).
Customer demographic responses: value text/ID with verified date.
Date-type demographic values where applicable.
Import dates for audit.

Behavioral & Engagement Data

Behaviors: names, types (Standard/Olytics/Event/White Paper/Webinar), action names/IDs, status codes, optional product reference, import date.
Customer behavior events: visit ID/date, promo codes, behavior text, IP, OS, device, browser, referring domain, page title, URL, and import date.
Visit attributes: attribute type, value, and free-text value.
Engagement scoring: frequency, recency, intensity, momentum, cluster membership.
Engagement clusters: cluster label/name, type code, and import date.

Email Engagement Data

Deployments: deployment name/ID, tracking number, deployment type, sent date, number of splits, campaign ID, list source, import date.
Recipient activity: unique communication ID, customer/email linkage, sent date, first/last open dates, opened count, clicked flag, unsubscribe flag, complaint flag, and import date.
Content/creative: subject, body text/HTML, from name/mailbox, message type, content sizes, split number, and import date.
Click tracking: click date plus link-level metadata (name, text, URL, tag, category name/value, tracking string) and import date.
Deployment classification: type name/designation, domain, status, and import date.
Contact filters/preferences: email address, deployment/deployment-type context, source, status (in/out/null), promo code, global flag, updated date, and import date.

Product & Subscription Data

Product catalog: product name, type, alternate ID, frequency, with import date.
Customer subscriptions: product subscription ID, customer/product linkage, version type, active/expire/original-order/verification dates, renewal count, amount, source/class IDs, product promo code, plus BPA type ID, related email address ID, payment status, postal address ID, and import date.
Paid orders (for subscriptions): order ID, related product subscription ID, active version, amount, expiration date, promo code, quantity, sales channel, source ID, order status, tax, term, group ID, and import date.

Personalization Impression Data

Personalization messages: message ID/name, start date, end date, and import date.
Customer personalization activity: activity date and type code, with customer/message linkage and import date.

Units & Unit Level Data

Units: unit ID/name, group ID/name, ABM/alternate ID, address and contact fields (street 1–3, city, region/region code, postal code, country/country code, phone), related product ID, created/changed dates, and import date.
Unit addresses (postal address records) with company, street/city/region/postal/country, status, and import date.
Unit–customer relationships with import date.
Unit demographics: demographic ID/value ID, value text, and import date.

Requirements

To enable access to the Analytical Data Warehouse via Snowflake Secure Data Share, you’ll need:

An active Snowflake account

Your Snowflake account identifier

Your Snowflake account located in the same region as Omeda’s Snowflake instance - AWS-US-EAST-2 (AWS US East Ohio)

💡 Tip: If you’re ready to get started, reach out to your CSM to begin the setup process.

Schema Changes

The Omeda Analytical Data Warehouse schema may evolve over time. Updates fall into two categories:

Non-breaking changes (no query changes required)

Adding a new table or view

Adding a column to an existing table or view

💡 Best Practice: Avoid SELECT * queries. Instead, list needed columns explicitly or create views to protect against unexpected schema changes.

Breaking changes (may require query updates)

Removing a table or view

Removing a column from an existing table or view

Changing the data type or nullability of a column

Omeda will provide advance notice and a migration period whenever possible for breaking changes.

Support

For setup requests, questions, or troubleshooting, contact your Omeda CSM. They will coordinate with our Data Warehouse team to ensure you have secure, reliable, and up-to-date table schemas and requirements.