In this post we introduce some important new features that are now available with Oracle Autonomous Data Warehouse, or ADW for short. There will be plenty more material that goes into these in far more detail, but this is a brief overview of these new capabilities.
We've made the database administrators’ life easy with Autonomous Data Warehouse, taking the drudgery out of the job, and freeing up time to do higher value work. But there are other users of the database–data analysts, application developers, and data scientists, too. We didn't think it was fair to leave them out in the cold, so we've built a suite of tools right into Autonomous Data Warehouse, to extend the benefits of autonomy to these users as well.
Want to learn more? Read Autonomous Data Warehouse: New Innovations for Data Analysts, Citizen Data Scientists and LoB Developers blog.
As an ADW user, it's all right there, at your fingertips, with nothing more to buy and nothing more to install. We know you've got a plethora of data sources to wrangle–apps, databases, files and more–and a vast array of things you want to do with all that data. We know you expect more than a flashing SQL prompt to do it all with–and the Autonomous Data Warehouse tools we aim to include everything you need and nothing you don't. There are tools for Data Load and Transformation, Business Modeling (to make sense of all that data), Data Insights (so you don't need to go looking for a needle in a haystack). There's a Catalog too, and tools for Machine Learning, Spatial, and Graph; plus of course SQL Developer Web and APEX for low-code application development.
So get ready, because the next time you log into your Autonomous Data Warehouse, you'll see a far richer tool palette awaits you after you click on the new Database Actions card.
Over time we'll be adding more and more tools to this suite. I want to talk about a few of them now, just to whet your appetite.
There's a Data Load tool. Anyone that's tried to load data knows that it's more easily said than done–until now, that is.
Just say what you want to do–load data into your ADW, link to data in a remote location or even set up a live feed. Then say where your data is–in a local file, a remote database or in an object store in some Cloud–and press go. That's it. Future posts will explore this feature in more depth.
Sometimes your data is just right, and sometimes you need to clean it up a bit–sometimes quite a lot. That's where the Data Transforms tool comes in.
All the power of Oracle Data Integrator (ODI), with a nice, clean, modern web-UI simple enough even for me to use. It's just what you want. Drag and drop to say what you want to do. Don't worry about how to do it–the tool does all that hard work for you.
So, what's next? You can only go so far looking at raw data. Before long you want a semantic model on top of it. That's where our Business Model tool comes in.
We've made it simple to build sophisticated models on your data, by identifying dimensions, hierarchies and measures; with a nice clean way of saying how to aggregate–sum, average or whatever. But wait, there's more. We make it fast, too! Simple SQL written against the business model is re-written to ensure optimal data access, and because we know about the hierarchical structure of the data, we can pre-aggregate the totals and sub-totals you want, before you've even told us you want them!
Next comes a feature I wanted to call the electromagnet, but they didn't let me. An analyst's job can often feel like looking for a needle in a haystack. So, throw the switch and all that metallic stuff is going to slam up on to that electromagnet. Sure, there are going to be rusty old nails and screws and nuts and bolts, but there are going to be a few needles as well. It's far easier to pick the needles out of these few bits of metal than go rummaging around in a pile of hay–especially if you have allergies! That's more or less how our Insights tool works. Load your data, kick off a query and grab a cup of coffee. Autonomous Data Warehouse does all the hard work, scouring through this data looking for hidden patterns, anomalies and outliers. We run some analytic queries that predict expected values, and where the actual values differ significantly from expectation, the tool presents them here.
Some of these might be uninteresting or obvious, but some are worthy of further investigation. You get this dashboard of various exceptional data patterns.
Drill down on a specific bar chart in this dashboard, and significant deviations between actual and expected values are highlighted.
Data is Capital and the built-in Catalog allows you to maximize its value. Data Lineage and Impact Analysis are now at your fingertips in this integrated tool.
Can you imagine buying a new car without Bluetooth, airbags, electric windows or cup holders? These are among the basic requirements in the modern generation of cars, but when I was a boy, cars didn’t even have seatbelts!
With Autonomous Data Warehouse we usher in a new generation of Database Cloud Service. And with this new generation, just as with previous generations of database, we raise the threshold for minimal required functionality. Autonomous operations by themselves represent a generational breakthrough, but we believe that more is required. For this new generation of database cloud service–the autonomous generation–all users of the service require embedded functionality. All this should be built in, with nothing more to buy and nothing more to install.
This was just a brief introduction, but look out for more content for detailed coverage of these various new capabilities. Click here to learn more about Autonomous Data Warehouse, or if you’d like to try it for yourself, visit Autonomous Data Warehouse Get Started Page.
Patrick Wheeler joined Oracle in 1986. He worked as Consultant and Consulting Practice Manager for several large financial institutions in London and San Francisco. He was closely involved with the development of Oracle CASE and was responsible for the worldwide launch of CASE and establishment of custom consulting practices in the early nineties.
In 1995, Mr Wheeler joined Siebel Systems and was a member of the Founder's Circle. As Data Architect and Director of Data Modeling he oversaw the development of Siebel's Data Model from Sales Force Automation startup to CRM juggernaut. He went on to establish the Reliability Engineering practice at Siebel Systems, guiding the product from pre-release instability to production release solidity. He has been awarded three US patents as Inventor.
Now back at Oracle, Mr Wheeler is Senior Director, Product Management, Oracle Database, with responsibility for Oracle Multitenant.