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Posts labeled Oracle Analytics

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AI Data Platform Series: Gold Layer Vehicle Movements
September 23, 2026

AI Data Platform Series: Gold Layer Vehicle Movements

Gold Layer: stations reference data and live bus positions for line 25, interpolated along TfL's own route geometry and shown on an Oracle Analytics Cloud map

This is the seventh post in the AI Data Platform series. The previous post built tfl.gold.arrivals_board, a live view of the next buses at every stop, and connected it to Oracle Analytics Cloud. What it couldn't do was say where anything is: TfL's arrivals feed carries stop IDs and names, but no coordinates. This post adds them. First a reference table with the location of every stop on every bus line, then an estimate of where each bus on line 25 actually is right now, drawn along the real road it drives on.

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AI Data Platform AI Data Platform Series AIDP Data Engineering Geospatial Maps Medallion Architecture OAC Oracle Analytics Posts Real-Time Data Pipelines Spark Structured Streaming
AI Data Platform Series: OAC MCP Server and Streaming Data
September 23, 2026

AI Data Platform Series: OAC MCP Server and Streaming Data

Asking the live data: Claude connected to Oracle Analytics Cloud through the OAC MCP server, querying the AIDP gold layer fed by live TfL streams

This is the eighth post in the AI Data Platform series. Over the last two posts, the gold layer grew three live pieces: an arrivals board for every bus stop in London, a stations reference table, and estimated positions for every bus on line 25, all queryable from Oracle Analytics Cloud. Until now, every question I asked that data was either SQL in a notebook or a workbook in OAC. This time I connected Claude to OAC through Oracle Analytics' MCP server and simply asked, in plain language, what was going on.

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AI Data Platform AI Data Platform Series AIDP Claude Generative AI Logical SQL MCP Model Context Protocol Natural Language Querying OAC Oracle Analytics Posts
AI Data Platform Series: Gold Layer Arrivals Board
September 23, 2026

AI Data Platform Series: Gold Layer Arrivals Board

Gold Layer: the arrivals board, a live SQL view over tfl.silver.arrivals_silver, connected to Oracle Analytics Cloud through the Oracle AI Data Platform connection type

This is the sixth post in the AI Data Platform series, following Just Streams: Real-Time Data Pipelines on OCI (the series intro), Setting Up the AI Data Platform Environment (stage 0), OCI Streaming and the Stream Producer (stage 1), Bronze Layer: Spark Structured Streaming (stage 2), Silver Layer: Spark Structured Streaming (stage 3), and the Comparing Bronze and Silver with DBeaver detour. Silver gave me one clean, always-current row per bus, stop, line and direction. This post covers the first half of stage 4: turning that into a live arrivals board, the kind you see on a screen at a bus stop, and putting it in front of Oracle Analytics Cloud.

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AI Data Platform AIDP Data Engineering Delta Lake Maps Medallion Architecture OAC OCI Oracle Analytics Posts Real-Time Data Pipelines Spark SQL
AI Data Platform Series: Just Streams: Real-Time Data Pipelines on OCI
August 06, 2026

AI Data Platform Series: Just Streams: Real-Time Data Pipelines on OCI

Together with Sandi Holub, I presented "Just Streams: Real-Time Data Pipelines in OCI – With a Live Demo Twist" at the Make IT 2026 conference in Portorož (28 May 2026). Sandi and I actually first presented this same session at the UKOUG conference in Birmingham back in December 2025, but it's only now that I've found the time to write more about it.

We deliberately kept the slide deck short and let a live demo carry most of the session. This post is the written recap of that story, and also the opening post in a series where I'll break the solution down layer by layer.

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AI Data Platform AIDP Data Engineering Events MCP Model Context Protocol Natural Language Querying OAC OCI Streaming Oracle Analytics Posts Real-Time Data Pipelines Spark Structured Streaming
Oracle Analytics meets AI Blog Series
July 15, 2026

Oracle Analytics meets AI Blog Series

Oracle Analytics Meets AI Series

This is a summary blog post with references to posts published under the Oracle Analytics Meets AI theme.

In this series, I am looking at how AI capabilities are becoming part of the Oracle Analytics experience: from asking questions in natural language, to using AI directly inside workbooks, and then extending the experience with more focused AI Agents.

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AI OAC Oracle Analytics Series
Oracle Analytics meets AI: Working with AI Agents
July 15, 2026

Oracle Analytics meets AI: Working with AI Agents

Oracle Analytics AI Assistant and AI Agents

This post explores how AI Assistant and AI Agents in Oracle Analytics Cloud (OAC) bring conversational analytics directly into the analytics experience. It shows how the AI Assistant supports natural-language exploration and visualization creation inside workbooks, while AI Agents extend this with dedicated instructions, business context, and knowledge documents. Together, they provide a path from quick conversational analysis to reusable, governed, domain-specific analytical assistants.

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AI Agents AI Assistant Ask BI auto insights data visualisation Natural Language Querying OAC Oracle Analytics oracle analytics cloud Posts
Oracle Analytics meets AI: Talk to Your Data with Ask BI
July 15, 2026

Oracle Analytics meets AI: Talk to Your Data with Ask BI

Ask BI - Oracle Analytics meets AI

This post introduces Ask BI, Oracle Analytics Cloud’s conversational interface for exploring data using natural language. It shows how users can ask business questions, refine their analysis through follow-up prompts, and receive answers and visualizations based on governed OAC data. Ask BI makes analytics more accessible by allowing users to focus on the questions they want answered rather than on how to build the analysis.

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Ask BI data governance data visualisation Natural Language Querying OAC Oracle Analytics oracle analytics cloud Posts Semantic Modeler
Semantic Model Series 9: Data Fragmentation
August 17, 2025

Semantic Model Series 9: Data Fragmentation

Oracle Analytics Bootcamp

In part 9 of the Semantic Model Series, I dig into data fragmentation in Oracle Analytics — splitting large fact tables like yearly revenue into separate logical table sources instead of one giant table. I walk through setting up fragmentation content filters, and compare the "Combine with Other Fragmented Sources" and "Enable Data Driven Fragment Selection" options with real generated SQL to show exactly how each one changes query behavior.

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