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

tkprasad

I would appreciate insights on the following

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

RVohra

@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

Brendan T

you could also utilize OCI Data Flow SQL Endpoints