Oracle has updated its Autonomous Database offering in an effort to maintain its lead over competing cloud-based database services from rivals such as AWS, Google Cloud, IBM, and Snowflake. Oracle Autonomous Database is an Oracle Cloud Infrastructure (OCI) service.
Oracle Autonomous Database, which is based on Oracle’s proprietary relational database management system (RDBMS), currently Oracle Database 23c, supports both transactional and analytical workloads.
The key differentiator for Autonomous Database is that its underlying management system automates patching, upgrades, and tuning, handling all routine database maintenance tasks without any manual intervention.
Autonomous Database supports four distinct workloads, including transaction processing, analytics and data warehousing, transactions and analytics on JSON data, and APEX application development, which is a managed low-code application development platform for building and deploying data-driven applications.
The latest updates include support for conversations in Select AI, new spatial enhancement in Oracle Machine Learning, a no-code model monitoring interface, and a new interface for Autonomous Database Graph Studio.
Select AI gets support for conversations
Oracle has added support for conversations to Select AI. Select AI, which was introduced in September last year, allows enterprise users to analyze their data using natural language, and with the help of large language models (LLMs) accessed via the OCI Generative AI service.
However, prior to this update, Select AI didn’t have the ability to remember previous questions, or allow users to ask follow-up questions.
“Select AI now makes that chat history available to the LLM so that it can interpret the context of follow-up questions. Users can now have a ‘conversation’ with their database to explore and narrow down the answers they need,” George Lumpkin, vice president of product management at Oracle, wrote in a blog post.
Enterprise users also can ask Select AI to produce the generated SQL and a description of the query processing, Lumpkin said.
The new capabilities of Select AI, according to research and advisory firm ISG’s executive director David Menninger, will help to ease the burden on developers and improve productivity.
“Without the context of a previous query, it means an enterprise developer would have to repeat a problem or request, modifying it as appropriate. That would quickly become frustrating and defeat the whole purpose,” Menninger said, adding that previously Select AI could just generate a basic structure of the SQL query from a natural language input.
dbInsight’s principal analyst Tony Baer believes that the new update takes AI copilots, or AI systems for coding, to a new level. “In addition to generating code, the language model must also ‘understand’ the logical structure of the database plus the unstructured text that is descriptive metadata. It must have a thorough understanding of query optimization as well,” Baer said.
Although Oracle’s Select AI competes with a growing number of natural language query services, including Microsoft Copilot, Github Copilot, Amazon Q, Snowflake Copilot, and Databricks IQ, Baer believes that Select AI’s differentiator is that it can understand highly complex schemas that are typical of Oracle Database deployments.
Select AI is accessible to any SQL application and is available as an integrated feature within Autonomous Database, the company said.
No-code interface for model monitoring
In order to help enterprise staff handling machine learning operations (MLOps), Oracle also has added a new no-code interface for monitoring machine learning models.
The new model monitoring interface, according to Oracle’s Lumpkin, not only will allow enterprise users to monitor models but also tweak them if required.
“For developers, improvement in model performance has long been sought and is a top priority. Examples include climate and weather modeling and improving public safety responsiveness,” said Ron Westfall, research director at The Futurum Group.
“The new capability enables Oracle’s Autonomous Database to streamline the modeling process in relation to competing offers,” Westfall added.
In addition, the company has introduced a new spatial enhancement in Oracle Machine Learning for Python, included as part of the Autonomous Database offering, that enables enterprises to include location relationships in machine learning models for improved model accuracy.
“Data scientists can detect spatial patterns through a quantitative approach—such as spatial clustering, regression, classification, and anomaly detection—without moving the data outside the database or writing complex algorithms on their own,” Lumpkin wrote.
In order to help enterprises gain more insights from their data, the company has added a new user interface for Autonomous Database’s Graph Studio that will allow enterprise to create property graph views on resource description framework (RDF) knowledge graphs using a drag-and-drop method.
“RDF knowledge graphs help apply meaning to data relationships by capturing complex associations across data in institutional silos. Enterprises can get additional insights from the data within the knowledge graphs,” Lumpkin wrote.
Oracle ahead of its database rivals?
The new updates, which are focused on managing data, generating insights from data, and speeding application development with the help of machine learning and AI, might give Oracle a leg up against rivals, according to ISG’s Menninger.
“Oracle made bold claims years ago when it introduced Autonomous Database. At the time it was probably ahead of itself in terms of what was realistic, but the technology is catching up and making more autonomous capabilities realistic. As a result of Oracle’s early investment, I think it’s safe to say they have established a lead in providing autonomous database capabilities,” Menninger said.
ISG’s executive director further said that any database that has the potential to offload administration and management drudgery would attract enterprises, as no one wants to spend their time in these activities. “As long as the features work and are priced appropriately, they make sense for all enterprises,” he added.
These features and updates also follow the trend of software providers building AI-based capabilities inside their database offerings, thereby curtailing the need for enterprises to move their data to a separate database or data platform to develop AI or generative AI-based applications.
“The next wave of disruption in the DBMS market will be the emergence of the data ecosystem as the overall data platform,” market research firm Gartner said in a report.
Westfall from The Futurum Group believes that Select AI has put Oracle’s Autonomous Database at the forefront of data platform innovations.
“With Select AI, I see that Oracle is breaking AI ground with a generally available capability for organizations to have a contextual dialogue with their private, proprietary data—intuitively. From the demos it is simple to use so that enterprises of all sizes can use it immediately,” the research director said.
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