Technology company Oracle released the latest updates of its Oracle Autonomous Data Warehouse, which is designed to transform cloud data warehousing simplifying it to a point-and-click, drag-and-drop experience for data analysts, citizen data scientists, and business users.
Data warehouse maintenance involves layers of processes that require skilled professionals to perform data loading, data transformation and cleansing, business modeling. With the emergence of the low-code and no-code approach, the updates on Oracle Autonomous Data Warehouse further amplify its significance in the future of computing.
“Oracle Autonomous Data Warehouse is the only fully self-driving cloud data warehouse today,” said Andrew Mendelsohn, EVP, database server technologies, Oracle, in a statement. “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.”
The amount of data generated every day from connected devices go petabytes upon petabytes. Oracle Autonomous Data Warehouse, a self-driving cloud data warehouse, not only simplifies data processing but also promises to deliver deeper analytics and tighter data lake integration.
The drag and drop feature business analysts to load and transform data on their own on the cloud using their own devices. Data processing to build new business models or analyze data for any discrepancy is so much simpler than before. Simply put, there is no more waiting time for skilled technical people to process those data.
Automation is now the backbone of any back-end and front-end business processes. Oracle Autonomous Data Warehouse utilizes machine learning models (AutoML UI) which provides a no-code user interface that significantly streamlines tasks of analysts and enables even non-experts to leverage machine learning.
Oracle Machine Learning for Python: Data scientists and other Python users can now use Python to apply machine learning on their data warehouse data, fully leveraging the high-performance, parallel capabilities and 30+ native machine learning algorithms of Oracle Autonomous Data Warehouse.
DevOps and data science teams can deploy and manage native in-database models and ONNX-format classification and regression models outside Oracle Autonomous Data Warehouse, and can also invoke cognitive text analytics. Application developers have easy-to-integrate REST endpoints for all functionality.
Graphs help to model and analyze relationships between entities (for example, a social network graph). Users can now create graphs within their data warehouse, query graphs using PGQL (property graph query language), and analyze graphs with over 60 in-memory graph analytics algorithms.
Graph Studio builds on property graph capabilities of Oracle Autonomous Data Warehouse to make graph analytics easier for beginners. It includes automated creation of graph models, notebooks, integrated visualization, and pre-built workflows for different use cases.
Oracle Autonomous Data Warehouse extends its ability to query data in Oracle Cloud Infrastructure (OCI) Object Storage and all popular cloud object stores with three new data lake capabilities: easy querying of data in Oracle Big Data Service (Hadoop); integration with OCI Data Catalog to simplify and automate data discovery in object storage; and scale-out processing to accelerate queries of large data sets in object storage.