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Oracle Data Miner review

#16 of 26 in Data Science and Machine Learning Platforms

A visual modeling extension for Oracle teams that prepare, compare, and score models in SQL.

7.9/10Editor score
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Oracle Data Miner7.9 Visit Oracle

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Oracle Data Miner is a graphical extension to Oracle SQL Developer for analysts and data scientists working with Oracle Database data. It brings data exploration, preparation, machine-learning model building, evaluation, comparison, and scoring into visual workflows. That makes it a fit for Oracle Database users who want to build and apply models within their existing database environment, rather than start with a notebook-centered or database-neutral platform.

The workflow editor covers a substantial sequence of modeling tasks: users can graph data with histograms, summary statistics, scatterplots, and boxplots; transform data through binning, recoding, missing-value treatment, and engineered features; and select influential attributes for supervised and unsupervised learning. It can build, test, evaluate, and compare multiple models, then apply models to new data and score data in SQL. Saved workflows can be rerun and scheduled. Generated SQL and PL/SQL scripts, along with a workflow API, also provide ways to invoke work beyond the visual editor. These strengths matter most where Oracle Database is already the center of the analytical process.

Its Oracle dependency is also the main reason to consider alternatives. Installation requires a Data Miner repository in a supported Oracle Database, and the product relies on Oracle Machine Learning capabilities embedded in the database. Oracle Data Miner is separately positioned as a SQL Developer extension, while Oracle Database requires a separate license; according to Oracle's pricing approach, Data Miner itself is contact-sales rather than a standalone published-price product. Integrations include Oracle Database, Oracle Autonomous AI Database, and Oracle Machine Learning for R. Choose it when visual preparation, in-database scoring, and repeatable workflows align with an Oracle estate. Teams seeking a broader notebook environment or a less Oracle-centered workflow should look elsewhere.

Oracle Data Miner pros and cons

  • Where it wins
    • Drag-and-drop workflows cover data preparation through model evaluation.
    • Compare machine-learning models and score new data with SQL.
    • Schedule repeatable workflows and generate SQL or PL/SQL scripts.
  • Where it doesn't
    • Requires an Oracle Data Miner repository in a supported Oracle Database.
    • Depends on a separately licensed Oracle Database.
    • Its focus is Oracle-centered rather than a broad notebook environment.

Oracle Data Miner fact sheet, pricing and score →

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