Oracle Autonomous Data Warehouse Gets Big Updates
Expect the innovations to benefit analysts, data scientists and cloud partners.
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The tools give business analysts a self-service environment for loading data. They also can easily make it available to their extended team for collaboration, Oracle said.
Simple dragging and dropping results in the loading and transforming of data from a laptop or the cloud. These analysts can then automatically generate business models. They can also quickly discover anomalies, outliers and hidden patterns in their data. Understanding data dependencies and the impact of changes also just got easier.
Oracle’s AutoML automates time-intensive steps you usually take when creating machine-learning models. It aims to increase the productivity of data scientists and improve model accuracy. Furthermore, it helps people without the expertise to use machine learning. Oracle says you can invoke AutoML via Python or via a no-code user interface.
Python aficionados can use the programming language to apply machine learning to their warehouse data. That means they can leverage the high-performance parallel capabilities that Oracle Autonomous Data Warehouse offers.
Further advancements mean a DevOps team or a data science team can deploy and manage native in-database models and ONNX-format classification and regression models outside Oracle Autonomous Data Warehouse. They can also invoke cognitive text analytics. And good news for app developers: They get what Oracle says are easy-to-integrate REST endpoints for all functionality.
There also are updates for graph-minded users. They can create graphs – for instance, a social network graph – within their data warehouse. They also have the ability to query graphs using PGQL (property graph query language) and analyze graphs with over 60 in-memory graph analytics algorithms.
Oracle says Graph Studio builds on the Autonomous Data Warehouse’s property graph capabilities to make graph analytics easier for novices. This includes automated modeling, integrated visualization and pre-built workflows for different use cases.
Oracle says Graph Studio builds on the Autonomous Data Warehouse’s property graph capabilities to make graph analytics easier for novices. This includes automated modeling, integrated visualization and pre-built workflows for different use cases.
Oracle on Wednesday unleashed a number of enhancements to its Autonomous Data Warehouse (ADS) that partners could find compelling.
Calling it the “only self-driving cloud data warehouse,” Oracle says the updates to ADS greatly simplify tasks for users. They transform a complex set of products, tools and tasks into an “intuitive, point-and-click SaaS application-like experience.”
The unveiling was the highlight of Oracle Live, the tech company’s virtual event.
Promising faster results and insights, Oracle says its Autonomous Data Warehouse is a single data platform. It governs all data from any source to run diverse analytical workloads, including departmental systems, enterprise data warehouses and data lakes.
There are also new self-service tools to easily prepare data sets and build machine learning models, the company said.
Our slideshow above takes you through the highlights of the new release.
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