Best practices for integrating Oracle AI Data Platform with Existing Data Lake Architecture?

Question

I would appreciate insights on the following

* What ate the recommended best practices for integrating Oracle AI Data Platform with an existing data lake Architecture arch?
* How does it handle data infestion and real-time vs batch processing scenarios?

RVohra · Accepted Answer

@tkprasad , Welcome to the Oracle Analytics Community!

I can see there are three approaches how Oracle AIDP integrates with existing DL.

Approach #1:  Oracle AIDP → Data Lake

Oracle AIDP is the trusted source, lake consumes it and it prepares the data, the lake uses it.

* Your data lake is mainly for machine learning
* You want all reports and KPIs to match Fusion / OTBI
* You trust FDI as your “official numbers”

How it works:

Oracle AIDP cleans and organizes the data first.

Then you send that clean data to the lake.

Why this is good:

The lake gets clean, business-ready data.

KPIs stay consistent with OTBI .

Less confusion about “which number is correct”

Approach #2:  Data Lake → Oracle AIDP

Lake is primary, Oracle AIDP is enhanced with it. Lake does the heavy lifting, Oracle AIDP adds Fusion intelligence.

* You already have a strong enterprise data lake
* You combine Fusion with IoT, CRM, 3rd-party systems
* Most data engineering already happens in the lake

How it works:

The lake prepares non-Fusion data.

Then Oracle AIDP connects to that curated lake data for reporting.

Why this is good:

No duplicate transformation work

Governance stays centralized

Fusion + non-Fusion data can be reported together

Approach #3: Two-Way Integration

Oracle AIDP and Lake feed each other. It is complex and powerful but risky only for mature organizations.

* Very large enterprise
* Strong governance team
* Clear data ownership rules

Risk:

Data loops (data moving back and forth endlessly)

Different KPI calculations in different places

Complex maintenance