Oracle adds updates to Autonomous Data Warehouse solution

Technology firm Oracle on Thursday announced multiple updates to its Autonomous Data Warehouse solution, which is a cloud data warehouse that automates provisioning, configuring and backing up of data warehouses of companies.  

The enhancements include intuitive point-and-click, drag-and-drop experience for data analysts, citizen data scientists and business users, the cloud applications and platform services provider said in a statement.  

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Oracle said that with the updates, Oracle will provide a single platform for businesses to ingest, transform, store and govern data from diverse environments such as departmental systems, enterprise data warehouses and data lakes.  

“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,” Andrew Mendelsohn, executive vice president of database server technologies at Oracle, said.  

The solution would provide support for multi-model, multi-workload, and multi-tenant requirements, which reside in a single converged database engine, the statement said.  

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Some of the key capabilities include built-in data tools, which allows for tasks such as loading data from laptop to the cloud by drag and drop, automatic generation of business models and quicker detection of anomalies.   

Oracle also introduced a no-code user interact for automated machine learning, called as the Machine Learning AutoML UI.  Additionally, the Machine Learning for Python update will allow Python users to apply ML on their warehouse data.  

DevOps and data science teams will also be able to deploy and manage native in-database models, along with easily integrating REST endpoints, the company said.  

Other enhancements include graphs that will help model and analyse data with 60 in-memory graph analytics algorithms, and seamless access to data lakes through querying data in Oracle Cloud Infrastructure object storage through Hadoop. Citizen data scientists and analysts will also benefit from self-service graph modeling and graph analytics, the statement said.