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Andrew Mendelsohn, Executive Vice President, Oracle Database Server Technologies
Leverage the full power of your data with Oracle’s new enhancements to Oracle Autonomous Data Warehouse, the industry’s first and only self-driving cloud data warehouse. Oracle goes beyond all competitive offerings by completely transforming cloud data warehousing from a complex ecosystem of products, tools, and tasks that requires users to have extensive expertise, time and patience into an intuitive, point-and-click, SaaS application-like experience for data analysts, data scientists, and business users alike. Organizations of all sizes— from the smallest to the largest—can now lower costs while getting significantly more value from their data.
Our research shows that Oracle Autonomous Data Warehouse customers have achieved 63% reduced total cost of operations, while increasing the productivity of data analytics teams by 27%, with breakeven on their investment having occurred in five months.
Carl Olofson Research Vice President, Data Management Software
Find out how a self-service data warehouse based on Autonomous Data Warehouse complements self-service analytics. Learn how to increase analyst, data scientist and developer productivity while also helping the business reduce its dependency on IT.
Oracle Autonomous Database has an intuitive tool for loading data from a variety of file types and sources, including: spreadsheets, remote databases, or multiple clouds.
Quickly prepare and transform data in Oracle Autonomous Database with simple drag and drop capabilities.
Oracle Autonomous Database has a built-in tool for automatically creating business models, giving you a consistent view of the data.
Learn more about Oracle Autonomous Database data insights tool for discovering anomalies, outliers, and hidden patterns in your data.
Oracle Autonomous Database catalog helps you understand dependencies and the impact of changes in data.
With the Graph Studio Interface in Oracle Autonomous Database, you can model, create, query, and analyze your data as a graph. Watch this short demo to see its easy-to-use interface for visualizing data.
Learn how OML4Py leverages the database as a high-performance computing environment to explore, transform, and analyze data faster and at scale—all while keeping data in the database and using familiar Python syntax.
With Oracle Machine Learning Services, you can easily manage and deploy your in-database and third-party ONNX machine learning models with a REST API. Learn how to get model management and deployment along with cognitive text capabilities.
There is no additional price for all these features that are built-in to the Autonomous Data Warehouse. This is all part of our base service without any extra price. These tools are built-in to ADW with very easy to use GUI. You don’t have to install, configure or integrate or connect anything. Simply use the ADW console to access and use these tools. We have some simple labs and guides to help you get started at: oracle.com/goto/adw-get-started.
Oracle Data Integrator and the data transformation tool in the Autonomous Data Warehouse (ADW) are very complementary. If you're familiar with ODI, indeed, the engine that we use for the transforms tool is ODI itself. So with that we inherit all of the richness of ODI, which is a mature, proven, scalable, tested, trusted product. And that's, the fundamental technology behind this transforms capability. The major difference is that we've greatly simplified the user experience with the data transform capability, it's now web based with a drag and drop interface, you no longer need to install a dedicated client. Although, if you would like, ODI still works and integrates with ADW. But with this new web based client, it's very nicely built-in simplified experience to empower business users.
We're building a suite of tools into Autonomous Data Warehouse. But our philosophy is to be extremely open in this platform, we want our customers to be able to use the tool with which they're most comfortable. And if they're already comfortable using another integration tool, then they can continue using that. That's going to work perfectly with Autonomous Data Warehouse as well. All of these tools are complimentary to the ones that you know and love. It doesn't need to replace anything. But we encourage you to try the built-in tools with Autonomous Data Warehouse for their simplicity, ease of use and broad functionality.
There's some very simple data profiling capabilities right at your fingertips after data load. Once you've loaded your data, there's a little explore card that pops up. Once you click on the Explore card, you can look at the data itself and just browse through it. Also look at the statistics there. This would give you a quick feel for what your data is like immediately on load. View the video here for more details – https://youtu.be/SJUw4wIvkS4
Beyond this Oracle provides other complementary services for data profiling to more levels of detail. For example, you could use enterprise data quality that gives you an enterprise class capability for doing data profiling. Or even other third party tools that you prefer. See here for list of other partner tools - https://www.oracle.com/autonomous-database/tools/.
The built-in data tools that we announced today we're making available inside of Autonomous Data Warehouse, and also inside of the other flavors of Autonomous Database. So we have Autonomous Transaction Processing and Autonomous JSON database along with the Autonomous Data Warehouse where these services will be available. Today they are available only on the shared deployment option. They are not on dedicated, but we have a path and we're going to be putting them on dedicated shortly along with on the Autonomous Database on Cloud at Customer. As far as the non-autonomous environment is concerned, we don't necessarily plan to put them there but, we might make some pieces of the tools available. Goal here is to take the Autonomous Database and really provide much more of a unified end to end, cloud- based experience around working with your data. And we strongly believe this should be sort of built in to a cohesive cloud experience.
To learn more about them I would suggest going to the Get Started page - www.oracle.com/goto/adw-get-started where you can watch demo videos and try out these services (OML, Graph, Data tools, Spatial etc.) using our free tier and the hands on labs that we have created to walk you through the step by step process. You can also learn more from our product pages - https://www.oracle.com/autonomous-database/autonomous-data-warehouse/ or contact our sales reps.
To integrate Autonomous Data Warehouse with data from E-business Suite, Salesforce, Netsuite, etc, we have built-in connectors that are now inside of ADW. These connectors actually originated from Oracle Data Integrator. Oracle Data Integrator solution is essentially being embedded into Autonomous Data Warehouse, and we are introducing new web-based UI's to create manage data transformations in Autonomous Data Warehouse at no additional cost.
AutoML can be applied to a wide range of machine learning type of problems. But if we're talking specifically about the Oracle Machine Learning’s (OML) AutoML UI, it supports both classification and regression machine learning techniques in this initial release. So when you think about classification, you know the business problems there, you might think of like, can we identify who our high value customers are or which customers are likely to churn or maybe in a healthcare setting, which patients are likely to need closer follow up care or are good candidates for a particular treatment? In the area of regression, these are business problems that include things like demand analysis, how much of a given product will be purchased, or maybe predicting home values based on a variety of MLS type data. So the in database algorithms that are currently supported by all OML4Py, are included in this analysis. And we have algorithms such as random forest and neural networks, GLM, just to name a few. And as part of our roadmap, OML development and Oracle labs are looking at some other machine learning techniques. And some of the candidates in the roadmap include things like time series and anomaly detection, so something to look forward to there.
There's a lot of synergies between these two products and they're actually quite complimentary. OCI Data Science focuses on accelerated Python development for expert data scientists on Oracle Cloud and Oracle Machine Learning (built-in the database), focuses on SQL R and Python. In-database machine learning enables enterprise scale analysis for data stored in an Oracle database, or Autonomous Database. Oracle Machine Learning also enables citizen data scientists in addition to data scientists by some of the interfaces that we've introduced, like the AutoML UI, or even the original Oracle Data Miner tool. With the introduction of OML Services, which supports Model Management and Deployment using REST endpoints, users not only get to deploy the OML in database models, but also third party models that are exported in any format. OCI Data Science, enables users to build models from popular tools like TensorFlow, MXNet, among others, and those can be exported in any format, and then immediately deployed into OML services. We're continuing to explore more areas of integration. And on our roadmap, we'll be including the OML4Py client module with those that are already available in OCI Data Science. And so this will enable users to build in database models directly from, the OCI Data Science’s Jupiter lab notebook environment, enabling a more seamless experience for OCI Data Science users to achieve in database scalability and performance.
Autonomous Data Warehouse has an OCPU per hour price metric (but the actual billing is pro-rated to seconds). When auto-scaling kicks in, your database may be using additional OCPU's when your workload spikes. You will be charged for the extra OCPU's at the standard rate -- and only for the extra OCPU's that your workload actually consumes.