- Provides easy-to-use, no-code tools that empower Data Analysts to do tasks that previously required data engineers and data scientists
- Puts faster, more powerful insights within the reach of organizations of all sizes
Today 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.
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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 capabilities.”
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What Analysts Are Saying
“Our research, based on interviews with several customers around the globe, shows that those Oracle Autonomous Data Warehouse customers have achieved approximately 63 percent reduced total cost of operations, while increasing the productivity of data analytics teams by 27 percent, with breakeven on their investment having occurred in an average of five months,” said Carl Olofson, Research Vice President, Data Management Software, IDC. “This ROI included significant productivity gains across data, analytics, and developer teams. While individual customer results may vary, the benefits found in this study are indicative of the kind of improvements that most may expect. With these new intuitive integrated tools incorporated in Oracle Autonomous Data Warehouse, it is reasonable to expect that productivity gains will further increase, enabling businesses to achieve an even better ROI.”
“Oracle Autonomous Database in all its flavors continues without a response from competitors even after three years in the market,” said Holger Mueller, Vice President and Principal Analyst, Constellation Research. “Now Oracle is adding to that lead with enhancements to Oracle Autonomous Data Warehouse that aim to democratize all aspects of analytics and machine learning by eliminating the need for users to know SQL. Instead, Oracle provides drag-and-drop UIs and AutoML for building and testing machine learning models, so that business users can do their own data explorations without depending on IT, DBAs, or system administrators to manage the data. All of this is built on Oracle’s converged database foundation which gives users access to all data models and types within a single database.”
“The objective of IT automation is to remove IT from the day-to-day workflows and allow the lines of business to work directly to define and mine the data that matters,” said David Floyer, CTO & Co-founder of Wikibon. “The Oracle Autonomous Data Warehouse now allows end-users to use drag-and-drop and low-code technologies to define the data requirements for a wide variety of end-user tools such as Tableau and Qlik. Oracle Autonomous Data Warehouse has improved spatial, graph, and ML analytics available on-premises or in public clouds with improved real-time performance. Oracle is cool again.”
“Oracle continues to make life dramatically easier for anyone associated with data and its value,” said Mark Peters, Principal Analyst & Practice Director, Enterprise Strategy Group. “Having started by helping DBAs and system administrators with its self-driving Autonomous Database, Oracle is now broadly extending the productivity and efficiency benefits of its Autonomous Data Warehouse so that everyone from data analysts, citizen data scientists, and business users can leverage it in easy and familiar ways. The drag-and-drop UIs and low-code interfaces simplify everything from data loading and analysis to building machine learning models. While Oracle’s competition—which often still requires extensive expertise, third-party tools or retrieving data manually from external databases—has work to do to better address the needs of non-technical personas, Oracle is there now.”
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“Enabling data analysts, citizen data scientists, and business users to create and analyze their own data sets with self-service tools avoids IT bottlenecks and significantly improves their productivity. This is exactly what Oracle has done with its enhancements to Autonomous Data Warehouse,” said Bradley Shimmin, Chief Analyst, Omdia. “Oracle is equipping integrated tools with intuitive drag-and-drop interfaces that make it easier for data analysts to load, transform, and clean data; further, they can leverage machine learning to automatically create business models and discover patterns, thereby generate insights—leading to better and faster business decisions.”
“Just as some data warehouse clouds are trying to figure out how they play well with machine learning, Oracle has moved the goal posts by a lot,” said Marc Staimer, President of DS Consulting and Wikibon analyst. “Oracle’s Autonomous Data Warehouse now includes Auto-ML. Oracle Autonomous Data Warehouse has included built-in machine learning since its inception. But now they’ve automated it so any Autonomous Data Warehouse customer can use it without any expertise. This makes other offerings seem rudimentary and primitive by comparison.”
“Oracle’s enhancements to Autonomous Data Warehouse are significant in three ways. First, it provides point-and-click user interfaces and machine learning automation, enabling non-professionals to generate actionable insights. Second, with this ease-of-use, even SMBs with small IT departments can get benefits from Oracle’s sophisticated cloud data warehouse. And, third, with Autonomous Data Warehouse, users can ingest data from any source from departmental systems to enterprise data warehouses, data lakes, and even from other clouds—AWS, Azure, and Google — and run diverse analytical workloads,” said Richard Winter, CEO and Principal Architect. “All in all, Oracle is materially extending the reach of Autonomous Data Warehouse across users, organizations, and data access to multi-clouds. This transcends the barriers of what is possible today with AWS Redshift and Snowflake and any other cloud data warehouse on the planet.”
“KuppingerCole has recognized Oracle’s continued innovation in database technologies, naming Oracle Autonomous Database the Overall Leader in our Leadership Compass on Enterprise Databases in the Cloud last year,” said Alexei Balaganski, Lead Analyst, KuppingerCole Analysts. “Clearly, the company did not stop there. With the unveiling of the improved Autonomous Data Warehouse, Oracle continues to deliver on its vision to democratize data management, analytics, and security for organizations of any size or industry. These new features and enhancements allow every user to access any data and obtain insights close to real-time with intelligent self-service tools. The company’s ‘converged database’ approach ensures that all types of data are accessible at once, as opposed to the siloed nature of traditional analytics platforms. This helps businesses to avoid the exposure of sensitive information to unnecessary security and compliance risks.”
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