Extracting Oracle Fusion data for Power BI – ETL & pipeline challenges?

[BI_is_Fun]

I’m fairly new to the Oracle Fusion ecosystem and want to learn from those who’ve integrated it into their analytics stack.

For anyone who has worked with Oracle Fusion data (via OTBI, BIP, APIs, or other extraction methods) and moved it into a data warehouse for downstream analytics (in my case, Power BI):

What are the biggest problems you’ve faced in this process?

1 On the reporting side (limitations in OTBI/BIP, missing subject areas, performance bottlenecks, query restrictions).

2 On the ETL/data movement side (API/data volume limits, refresh windows, data quality/completeness issues, integration complexity).

I’m interested in hearing lessons learned and what workarounds helped?

[v-prasare]

Hi @BI_is_Fun,

We would like to confirm if our community members answer resolves your query or if you need further help. If you still have any questions or need more support, please feel free to let us know. We are happy to help you.

Best Regards,
Prashanth Are

[v-prasare]

Hi @BI_is_Fun,

We would like to confirm if our community members answer resolves your query or if you need further help. If you still have any questions or need more support, please feel free to let us know. We are happy to help you.

Best Regards,
Prashanth Are

[Vinodh247]

I have not directly worked on Oracle Fusion but has seen similar ERP to analytics integrations (SAP, Dynamics 365, Workday, etc), here is my take:

General ETL / pipeline challenges (API driven ERP systems):

• API throttling and limits: ERP SaaS systems are notorious for request caps. You cannot pull millions of rows in one go. Instead, you design incremental pulls (last modified date) and chunk requests into pages.
• Data freshness: Even though the vendor markets APIs as “real time”, in practice refresh windows and processing delays mean you get data hours later. Most companies settle for near-real-time or daily refreshes.
• Data model complexity: ERP back-ends are highly normalised and module-driven. Keys are inconsistent across modules (ex: emp ID in HCM vs. finance). That makes reconciliation work necessary downstream.
• Hybrid extraction needed: No single path covers everything. Some data comes via APIs, some via scheduled bulk extracts, some via subject area reports. You need a central orchestration to blend them.

General lessons that apply no matter the ERP:

• Do not rely on the ERP’s built-in reporting for analytics. Use it just to get the data out.
• Expect to build a strong staging layer. This is where cleansing, reconciling keys, and harmonising modules happen.
• Plan for change. ERP vendors update APIs and subject areas frequently. Pipelines need regression checks and flexibility.
• Invest in orchestration. Whether ADF, Fabric pipelines, or another ETL tool, you need a single control point.
• Buy vs build decision is key. If budget allows connectors from Fivetran, Informatica, etc. can save months of custom work.

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