has convinced him of the strength of the technology. “Our destination today is autonomous,” said Sullivan, speaking of his team, whose customers process 60 billion transactions a day and hold more than 3 exabytes of data in Oracle databases.
Mendelsohn shared where his team is going next. “We’re taking the concept we’ve proven with the Autonomous Database—the whole exercise of eliminating the human labor around data management—and we’re extending that to help business analysts, data scientists, and developers build powerful analytics and data-driven applications,” he said. Below are five examples Mendelsohn gave for how Oracle is doing this.
2. Data virtualization capabilities are built into the Autonomous Database. These allow analysts to easily run high performance SQL queries across data in their autonomous database and the object store. There’s no need to write ETL code to consolidate the data before it can be queried. “It’s a really nice autonomous experience extended to business analysts and data scientists,” Mendelsohn said.
5. An AutoETL experience in the Autonomous Database will let analysts move data into a data lake, data mart, or data warehouse with much more ease. They can simply choose the data sources and transformations they want, and the ETL code will be automatically generated for them, said Mendelsohn.