Data has the potential to be an organization’s most valuable asset. But how do you reach that potential? How can you make data as easy to use and exchange as a common currency? And how can you do so without breaking the bank?
Making your data simple to use, reliable, economical, and secure has been the goal for Autonomous Database since its inception – and it’s been a journey for sure! It’s why we try to continuously innovate; in the last year alone, we released over a hundred new features. We take a lot of pride over the fact that your Autonomous Database automatically upgrades and your user experience gets better while your system keeps on running; Autonomous Database delivers on its 99.995% uptime promise.
"Customers face many obstacles when analyzing siloed data across on-premises, cloud, and SaaS applications, especially the lack of multicloud and data lake interoperability, and the need to assemble an array of disjointed tools and services to support the data analytics ecosystem. The latest Oracle Autonomous Data Warehouse innovations make it easier for customers to query, manage, share, and scale their data—regardless of location. We continue to push the boundaries of what’s possible in data management systems to deliver the performance, automation, and multicloud integration for all key database workloads and data types.
-- Çetin Özbütün, Executive Vice President, Data Warehouse and Autonomous Database Technologies, Oracle
We’re happy to announce important new capabilities being introduced into Autonomous Database that we think will make the service even easier to use and more open. Here we’ll give you the announcement summary and point you to other posts and demonstrations that provide more details. The new capabilities can be categorized into the following areas:
Most of our customers have data distributed across clouds and on-premise data centers. An integrated view of all that data is critical to better decision making. Autonomous Database simplifies multi-cloud data warehouses, with secure access to object storage in OCI, AWS, Azure, and Google Cloud, as well as direct database connections to Azure SQL, Azure Synapse, Amazon Redshift, Snowflake, MongoDB, Apache Hive and PostgreSQL. Oracle is expanding its offering to include query access to Apache Iceberg tables as well as integration with AWS Glue, to retrieve data lake schema and metadata automatically.
Simple, secure multi-cloud access to all types of data
Data lake sources can be complex:
Autonomous Database natively understands these complexities when accessing data lake sources. Integration with each cloud provider’s identity and access management helps ensure data protection. Simple APIs allow creating tables to access these sources through Oracle SQL – allowing you to seamlessly analyze data across your warehouse and data lake. Your applications are completely insulated from what’s happening under the covers; they simply operate as they normally would.
Autonomous Database has had deep integration with OCI Data Catalog for some time. Similar support for the AWS Glue catalog will be available soon. Data catalog integration dramatically simplifies administrative tasks. Data lake metadata is automatically synchronized with Autonomous Database, making data in the lake available immediately for query. You can think of the data lake as being a natural extension of the warehouse. You can run SQL queries that combine data stored in Autonomous Database with data stored in object store without physically moving the data.
As mentioned above, Autonomous Database offers a rich set of APIs for integrating and analyzing data. Data Studio is a built-in, self-service, no-code user experience that allows you to develop solutions without relying on IT:
For example, loading data from a spreadsheet, database or data lake is a simple, drag-drop operation. Information about the source is derived automatically:
Data Studio then kicks off a job to define your target tables and load the data from sources. You are then ready to start analyzing your data.
But what if your data source is more complex? It may be in an enterprise application like Salesforce or Oracle Fusion, a NoSQL store, or simply data available through a REST endpoint. And, that source data may require wrangling prior to combining it with other data sources or presenting it to users.
Significant updates to Data Studio have made integrating these sources dramatically easier. Data Studio now supports over 100 data sources across different types of stores; you can see a snapshot of supported sources below:
Along with the support for a wide range of sources, new transformation features let you wrangle the data to meet your requirements. Transformations are easy to configure and the job execution takes advantage of Autonomous Database performance and scalability.
There are a wide range of transform functions that can be applied to your workflow, including filters, aggregations, joins, lookups – even machine learning, spatial and graph functions. All of this is possible without writing a single line of code.
Everyone loves spreadsheets – always have, always will. But how often have you been in a meeting where the numbers in people’s spreadsheets don’t match?
Instead, make trusted decisions based on a single source of truth. Data Studio’s plug-ins for Microsoft Excel and Google Sheets make a direct connection to your data in Oracle Autonomous Warehouse. A simple wizard-based interface retrieves your organization’s dimensional data and metrics into Google Sheets pivot tables from built-in Analytic Views that's stored in the database. Both business definitions (e.g., hierarchies and metrics) and their data values are shared across all users and applications, ensuring consistency.
The advent of data lakes was a huge change for businesses. Low-cost storage made it possible to store and use data that would otherwise be discarded. Decoupling of compute and storage meant much more efficient use of resources. And open-source tools and frameworks like Spark and Python made it easier to load and transform data and use machine learning. The cost to experiment with new data was much lower and that opened up new possibilities.
But one thing didn’t change. The vast majority of analytics still uses SQL, whether it’s somebody writing SQL queries directly, or using tools like Power BI or Oracle Analytics Cloud that use SQL behind the scenes. You can use SQL with traditional data lakes, but the focus there has been on lowering cost, not optimizing for SQL performance. What if you could get all the benefits of a data lake, particularly the low cost of storage and separation of compute, but with dramatically faster performance?
Organizations can now rethink their data lake architecture and store all data required for SQL analytics in Autonomous Data Warehouse thanks to a 75 percent reduction in Autonomous Data Warehouse storage costs. Oracle Autonomous Data Warehouse’s highly optimized storage is now the same price as object storage, but delivers up to 20x faster query performance. This gives you flexibility in your data lake architecture: you can store all of your data in Autonomous Data Warehouse, in object stores, or both. You can choose the best architecture based on your requirements.
Traditional ways of sharing data are inherently complex and insecure. You unload data into a CSV file and then share the file via email or by copying it to shared. There’s no governance or security and no systematic refreshes or up-to-date view of data (see the spreadsheet dilemma above!).
Autonomous Database provides a much better way to share data with stakeholders both inside and outside your organization. Open Data Sharing allows data owners to create and manage Data Shares. You can think of a Data Share as a collection of data sets (or tables). Data owners define what goes into that share and who can read it. The Share Recipient receives a notification that data is available and then remotely accesses that centralized data. The data is secure, governed and always up to date. You can use a variety of tools to analyze that data; below, we’re showing a Power BI user analyzing data in the share.
Open Data Sharing revolutionizes the way you can work with shareholders. Your data can be treated as the valuable asset that it truly is. Coworkers and partners make better decisions using trusted data. You can monetize your data assets more easily than ever before.
This was just a quick summary of what’s new in Autonomous Database. We hope that these updates help you take advantage of all your data – your organization’s incredibly value asset!
We invite you to learn more about the latest Autonomous Database enhancements by visiting these resources:
George is Vice President of Product Management for Oracle Autonomous Database.