Oracle E-Business Suite and Marketo integration

What moves between them

Customer and opportunity records flow from Oracle E-Business Suite into Marketo. Customer accounts from EBS are synced to Marketo companies and leads so marketing teams can run campaigns against the correct accounts. Opportunity data from EBS GL postings and order headers map to Marketo opportunities for sales pipeline visibility. The sync runs on a schedule tied to your period-close or transaction cycle, not in real time, since EBS is pull-only.

How ml-connector handles it

ml-connector stores both credential sets encrypted. For Oracle E-Business Suite, it accepts the full hostname and port per customer (since EBS publishes no shared API gateway), validates each service endpoint in the Integration Repository at that instance, and refreshes the session token on 401. For Marketo, it exchanges the OAuth2 Client Credentials for a Bearer token before each batch of requests and reuses it until expiry. Customer records from EBS HZ_CUST_ACCOUNTS and GL headers are polled with LAST_UPDATE_DATE filters and offset pagination, then mapped to Marketo company and lead records. Opportunity data is extracted from EBS orders and GL documents and upserted to Marketo opportunities by matching on unique identifiers. Since both systems are pull-only, ml-connector polls on a cadence and tracks the last successful sync point so no records are missed. Every record carries a full audit trail and can be replayed if a downstream request fails.

A real-world example

A mid-market manufacturing and distribution company runs Oracle E-Business Suite for financials and procurement, and uses Marketo for demand generation and lead nurturing. Before the integration, new customer accounts and sales orders were manually entered into Marketo by the marketing operations team, lagging behind EBS by days and creating mismatches between what sales thought they had and what marketing was targeting. With EBS and Marketo connected, each new customer and opportunity in EBS flows into Marketo automatically on a daily schedule, so marketing campaigns hit the right accounts with current data, and the sales team can see Marketo engagement metrics tied back to actual EBS opportunities and revenue.

What you can do

• •Sync customer accounts from Oracle E-Business Suite into Marketo as companies and leads on a daily or custom schedule.
• •Flow opportunity records from EBS orders and GL documents into Marketo for sales pipeline visibility and campaign targeting.
• •Authenticate EBS via HTTP Basic Auth or session token and Marketo via OAuth2 Client Credentials, with automatic token refresh on expiry.
• •Poll both systems on a schedule tied to your transaction cycle, with offset pagination and LAST_UPDATE_DATE filtering to avoid duplicate syncs.
• •Track the full audit trail on every record sync and replay failed transactions without re-querying the source.

Questions

Which direction does data flow between Oracle E-Business Suite and Marketo?

The main flow is from Oracle EBS into Marketo. Customer accounts and opportunity records move from EBS into Marketo so marketing and sales teams can target and track the right prospects. Marketo is typically not written back to EBS, since Marketo is a marketing platform without financial or procurement data that EBS would need.

How does ml-connector handle EBS authentication and the lack of a fixed API gateway?

ml-connector stores the EBS hostname, port, and credentials encrypted and accepts them per customer, since EBS publishes no shared API base URL. It authenticates via HTTP Basic Auth or by obtaining and reusing a session token from the EBS login endpoint, and it refreshes the token on a 401 response to handle expiry within the typical 30 to 60 minute window.

Why does the integration poll instead of using webhooks?

Both Oracle EBS and Marketo are pull-only on the inbound side. EBS has no webhooks for cloud connectors, and Marketo webhooks only push outbound FROM Marketo to external systems. ml-connector polls both on a schedule with offset pagination and LAST_UPDATE_DATE filtering to detect new and changed records without re-fetching the entire dataset each time.