The introduction of Oracle’s Autonomous Data Warehouse service revealed the simplicity of provisioning a data warehouse, no matter the size of the organization, since it removes the need for operational administration. With a cloud data warehouse, basic operational tasks are accomplished, freeing IT teams to deal with adding more datasets and extending the data warehouse depending on what the organization needs.
In its initial stage, the service still required organizations to use SQL in building a warehouse. Now, the self-driving cloud data warehouse offers a simpler point-and-click drag-and-drop experience that targets data analysts, citizen data scientists, and even business users. This new set of self-service tools designed for non-SQL users will accelerate delivery of insights and data, while at the same time lowering the total cost of ownership (TCO) with zero administration.
“Oracle Autonomous Data Warehouse is the only fully self-driving cloud data warehouse today. With this next generation of Autonomous Data Warehouse, we provide a set of easy-to-use, no-code tools that uniquely empower business analysts to be citizen data scientists, data engineers, and developers,” said Andrew Mendelsohn, executive vice president for database server technologies at Oracle.
With improving ease-of-use in mind, the latest release will not only make the process of data warehousing more intuitive for business developers and analysts, it will also deliver deeper analytics and tighter data lake integration at the same time.
Business analysts do not need SQL knowledge to generate business models and discover anomalies since the self-service environment is built for loading data used for collaborative purposes. Loading and transforming data can easily be done on a laptop by simple drag-and-drop, revealing outliers and hidden patterns, as well as data-dependent relations and impacts in a shorter timeframe.
Machine learning models can also be created even by non-experts through AutoML, an automated no-code interface that deals with the time-demanding steps and improving model quality, increasing the productivity of data scientists. Applying machine learning to the warehouse data can also be done by using Python now.
Other updates include property graph query language support, memory graph analytics algorithms, automated graph model creation, Oracle Big Data Service (Hadoop) data query, OCI Data Catalog integration, and scale-out processing.
