AUSTIN, Texas, March 17, 2021 /PRNewswire/ -- 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.
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."
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."
"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."
About Oracle
Oracle offers suites of integrated applications plus
secure, autonomous infrastructure in the Oracle Cloud. For
more information about Oracle (NYSE: ORCL), please visit us
at oracle.com.
Additional Resources
- Watch Oracle Live: New Autonomous Data Warehouse
Innovations
- Read the Autonomous Data Warehouse technical blog
- Learn more about Oracle Autonomous Data Warehouse
- Try Oracle Autonomous Database
Trademarks
Oracle and Java are registered trademarks of Oracle
Corporation.
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SOURCE Oracle