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Oracle taps advanced machine learning to automate Oracle 18c

Advanced machine learning will drive Oracle Database 18c to cloud success, Larry Ellison claims. But an 'autonomous' cloud service based on 18c isn't a threat to DBA job security, he says.

SAN FRANCISCO -- Oracle founder and CTO Larry Ellison this week detailed an "autonomous" cloud service built on...

top of Oracle Database 18c that he said will rely on advanced machine learning techniques to greatly reduce database administration tasks, such as tuning and patching.

At the heart of the Oracle Autonomous Database Cloud offering is extensive use of machine learning, which Ellison called "the first branch of artificial intelligence that really works."

The machine learning functionality employs neural networks and other modeling algorithms to automate maintenance of databases. In addition, it can sift large amounts of log data and detect recurring patterns of database activity to help boost security, according to Ellison, who said the technology is also included in updates to the security monitoring and analytics service that's part of the Oracle Management Cloud.

Within Oracle Autonomous Database Cloud, machine learning enables the new Oracle Database 18c release to "patch itself while running, all without any downtime whatsoever," Ellison said in a keynote at the Oracle OpenWorld 2017 conference. He also said operations improvements allow Oracle to offer a service-level agreement that guarantees 99.995% database reliability and availability, while reducing planned and unplanned downtime to less than 30 minutes per year.

Automation of database administration

While the cloud service, which Ellison dubbed "self-driving" and "the world's first autonomous database," may be unique by some measures, it's also part of a long-standing trend that is well under way.

Larry Ellison, Oracle CTOLarry Ellison

Automation of database cluster deployment in the cloud has become increasingly common, and wider automation can be anticipated, according to Tony Baer, an analyst at Ovum. "You can see how cloud databases are doing automation -- with database sharding as a major example," Baer said.

Meanwhile, query performance and other database activities are also being affected by advanced machine learning technology, he said. "Oracle has all kinds of database activity logs," Baer noted. "That is big data that acts as a corpus for machine learning that can figure out what is a normal pattern, and highlight queries that are going to cause trouble."

Advanced machine learning adds another element to the mix, but the latest Oracle moves are best viewed as part of an evolution in process automation, according to Vinod Bhutani, database services manager at DBAMart, a consulting and technical services provider in Broomfield, Colo.

"There is a whole lot of automation for the database already. For example, there are such tools as Oracle SQL Tuning Advisor and Segment Advisor," Bhutani said in an interview at OpenWorld. "In my view, the database is 60% to 70% automated already." The amount of automation employed in Oracle environments is often based on a database administrator's comfort levels with such automation's effectiveness, he added.

Bhutani said he would be looking for additional details, particularly on Oracle's cybersecurity offerings, to see how much further the vendor takes database automation.

Whither the DBA?

In his OpenWorld keynote, Ellison admitted the move to greater automation in the Oracle cloud database service could be seen as a threat to DBA job security. But he was basically sanguine about the prospects. "Yes, you are automating the ways of database professionals, but they already have more work than they can possibly ever get to," he said.

Greater database automation will free up DBAs from routine patching and repetitive tuning, Ellison said, enabling them to focus more on database schema design, analytics -- including advanced machine learning styles -- and data security.

Forrester analyst Noel Yuhanna, on hand at the conference, agreed. "The DBA job is being changed toward more data-driven initiatives, with more emphasis on security and governance -- and architecting the future of the data," Yuhanna said. In the future, he predicted, "the DBA will focus more on business value, as opposed to technology."

Baer also pointed to an increasingly important role for the DBA. "There is definitely a future for the DBA," he said. "There's just no question about it. You can't automate everything."

Hearing Redshift steps

Ellison said the autonomous database service, with Oracle Database 18c running on Exadata infrastructure in the Oracle Cloud or in on-premises Cloud at Customer systems, will become generally available in December for data warehousing only; a transactional version is due to appear in June 2018.

Putting the data warehouse first emphasizes Oracle's intention to compete more fully with Amazon Web Services and its Redshift cloud data warehouse in particular. At OpenWorld, Ellison repeatedly cited Redshift as a competitor, claiming superior uptime and better relative pricing for Oracle.

"We guarantee our bill will be less than half of what Amazon Redshift will be," he said. "We will write that in your contract."

Oracle further moved to sweeten its cloud pricing deals recently, introducing a "bring your own license" policy for existing customers moving databases, middleware and more to the Oracle Cloud platform.

With the "18c" designation, Oracle takes on a model-year naming format for its flagship relational database, not unlike that of the one used for Microsoft SQL Server. Oracle has said it now plans to release updates to Oracle Database annually; also, aligning database naming with calendar years is in some part a bow to the growing use of yearly, subscription-based pricing models for databases on the cloud.

Next Steps

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How much automation is the right amount of automation for databases like Oracle's?
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At OOW, ML was the magic sauce that makes everything work. I don't buy it. It replaces the old diagram arrows that could be labeled "and then a miracle happens." ML takes a great deal of preparation by people, including researchers and data scientists. It needs appropriate training data, and the skill to devise it, and it has to proceed with learning for each problem domain. So don't wait underwater for ML to solve all of these problems any time soon.
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Agreed. "You're mileage may vary" is not just for cars!
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