Oracle announced a set of innovative enhancements to Oracle Autonomous Data Warehouse, the industry’s first and only self-driving cloud data warehouse. With this latest release, Oracle goes beyond other cloud offerings by completely transforming cloud data warehousing from a complex ecosystem of products, tools, and tasks that requires extensive technical expertise, time and money to perform data loading, data transformation and cleansing, business modeling, and machine learning into an intuitive point-and-click, drag-and-drop experience for data analysts, citizen data scientists, and business users. As a result, Oracle Autonomous Data Warehouse empowers organizations of all sizes—from the smallest to the largest—to get significantly more value from their data, achieve faster results, accelerate insights, and improve productivity while lowering costs with zero administration.
The latest enhancements to Oracle Autonomous Data Warehouse provide a single data platform built for businesses to ingest, transform, store, and govern all data to run diverse analytical workloads from any source, including departmental systems, enterprise data warehouses and data lakes.
“Oracle Autonomous Data Warehouse is the only fully self-driving cloud data warehouse today,” said Andrew Mendelsohn, executive vice president, database server technologies, Oracle. “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.”
Citizen data scientists and analysts will also benefit from powerful new self-service graph modeling and graph analytics. To empower developers to build data-driven applications, Oracle offers Oracle APEX (Application Express) Application Development, a low-code application development tool built directly into its cloud data warehouse, as well as RESTful services, which makes it easy for any modern application to interact with warehouse data. Unlike other vendors’ single-purpose, isolated databases in the cloud, Oracle Autonomous Data Warehouse provides support for multi-model, multi-workload, and multi-tenant requirements—all within a single, modern converged database engine—including JSON document, operational, analytic, graph, ML, and blockchain databases and services.
New Innovations in Oracle Autonomous Data Warehouse
The latest release includes many new innovations, not only a broad set of capabilities that make it easier for analysts, citizen data scientists, and line-of-business developers to take advantage of the industry’s first and only self-driving cloud data warehouse, but also features that deliver deeper analytics and tighter data lake integration. Key capabilities include:
Built-in Data Tools: Business analysts now have a simple, self-service environment for loading data and making it available to their extended team for collaboration. They can load and transform data from their laptop or the cloud by simply dragging and dropping. They can then automatically generate business models; quickly discover anomalies, outliers and hidden patterns in their data; and understand data dependencies and the impact of changes.
Oracle Machine Learning AutoML UI: By automating time-intensive steps in the creation of machine learning models, the AutoML UI provides a no-code user interface for automated machine learning to increase data scientist productivity, improve model quality and enable 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.
Oracle Machine Learning Services: 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.
Property Graph Support: 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 UI: 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.
Seamless Access to Data Lakes: 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.
What Customers Are Saying
“By using Oracle Analytics Cloud and Autonomous Data Warehouse, we’re able to apply machine learning and spatial analysis to better track check cashing behavior that mitigates risk and prevents fraud in real-time to help businesses and consumers more confidently engage in commerce,” said Eric Probst, Senior Manager, Fraud Analytics, Certegy.
“With Oracle Autonomous Data Warehouse and APEX, I not only have a world-class, scalable, super-secure, super-powerful database engine, but with the built-in application development tools, I can also build and deploy applications almost right away so that I can get people access to data,” said Frank Hoogendoorn, Chief Data Officer, MineSense. “I don’t know of any other platform where I can do that out of the box.”
“Having innovative capabilities for loading data that’s built right into Oracle Autonomous Data Warehouse should save us a tremendous amount of time,” said Derek Hayden, SVP of Data Strategy and Analytics, OUTFRONT Media. “The declarative extract, load, and transform with its drag-and-drop functionality will enable us to quickly load and transform multiple data types, and see the relationships within the data through the auto-insights capability.”
“Oracle Autonomous Data Warehouse has reduced time-to-market for a typical data warehouse project from three months to three days, while delivering deeper and more actionable insights,” said Steven Chang, CIO, Kingold. “Being able to benefit from increased automation for data ingestion, transformation, building business models and getting insights is excellent news, and we’re looking forward to using those capabilites
