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The new release has a simplified architecture to make it more accessible.

This week Oracle announced a new release of its cloud-based Autonomous Data Warehouse service. This latest release adds new capabilities that analysts are praising for rendering machine learning accessible for all users.

This week’s release basically moves the Oracle Autonomous Data Warehouse from a complex constellation of products into an “intuitive point-and-click, drag-and-drop experience,” according to the company. The platform has been made simple enough for both data analysts and business users to use.

About the Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is a cloud data warehouse service that eliminates all the complexities of operating a data warehouse. This includes securing data and developing data-driven applications. It automates provisioning, configuring, securing, tuning, scaling, and backing up of the data warehouse.

The warehouse includes tools for self-service data loading, data transformations, business models, automatic insights. It also sports built-in converged database capabilities that enable simpler queries across multiple data types and machine learning analysis. Oracle makes the service available in both its public cloud and in customers’ own data centers.

Related: Oracle expands next generation cloud in Europe

New features make life easier for non-techies

Today’s release adds new capabilities and features that make life easier for data analysts and regular business users. It will also enable deeper analytics than before, according to Oracle.


Of all the new features, however, analysts are especially excited about AutoML. This machine learning tool helps to automate many of the time-consuming steps involved in creating machine learning models. And it does this through a no-code user interface or for the more advanced user via Python.

The service is now compatible with the Python programming language. Python is a programming code that is more intuitive to write and is understandable by humans. This makes it easier to use to build ML models.


Oracle also added support for Graphs. Graphs can help to analyse the relationships between entities. Oracle added over 60 in-memory graph algorithms to do analytics with. Users can create graphs in the Graph Studio UI and make use of those algorithms. The Graph Studio UI is easy to work with, even for non-technical users.

This latest release of Oracle’s Autonomous Data Warehouse is out now.