Building an AIDP environment on OCI, layer by layer — from workbench to bronze/silver/gold pipelines.
More →From asking questions in natural language to AI Agents inside Oracle Analytics.
More →A practical end-to-end guide to building and managing semantic models in Oracle Analytics Semantic Modeler.
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This is the first technical post in the series that opened with Just Streams: Real-Time Data Pipelines on OCI. That post walked through the whole TfL-bus-arrivals demo Sandi Holub and I gave at Make IT 2026 and UKOUG 2025. This one goes one layer down: the piece that gets live data into the platform in the first place — a small Python producer and an OCI Streaming topic.
On 26 August 2026 in Ljubljana, I took part in the Oracle Technology Roundtable, where I presented together with my SmartQ colleagues Mojca Gros and Grega Dvoršak.
For the event, we prepared a joint presentation titled “Oracle Analytics & AI”, with the subtitle “Oracle Private Agent Factory in Oracle Analytics Server.” The session brought together three closely related topics: the latest Oracle Analytics Server platform, the use of Model Context Protocol with OAS, and Oracle AI Database Private Agent Factory.
This is summary blog post with references to posts published under AI Data Platform theme:
This post backs up a step from the rest of the AI Data Platform series. Before any bronze/silver/gold Spark job can run, or a stream producer can hand data off to something downstream, the AI Data Platform (AIDP) environment itself has to exist: a Workbench, a workspace, a catalog, compute, and a place to keep secrets. This post walks through building all of that from nothing on a brand-new OCI tenancy, using the same TfL bus-arrivals demo's naming (catalog tfl, schemas bronze/silver/gold) as the throughline.