Database major Oracle has brought a new set of capabilities to its cloud-based Autonomous Data Warehouse service with a fresh update that can potentially democratise machine learning.
The update will make the cloud data warehouse an intuitive point-and-click, drag-and-drop experience for data analysts, citizen data scientists, and business users, the company claimed in a statement.
The company said the product will help organisations to derive more value from their data, gain insights faster, improve productivity and lowering costs. The new update will offer a single data platform for businesses 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. 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,” Andrew Mendelsohn, Executive Vice President, Database Server Technologies, Oracle, said.
It brings in new self-service graph modelling and graph analytics for citizen data scientists and analysts, while developers can leverage the low-code application development tool to build applications. Named Oracle APEX (Application Express) Application Development, the developer tool is built directly into its cloud data warehouse as well as RESTful services, which enables direct interaction between modern applications with warehouse data.
Here’s a sneak peek into its key capabilities:
Built-in Data Tools: It offers a self-service environment to business analysts which enables them to load data and make it available to their extended team for collaboration. They can load and transform data from their laptop or the cloud by dragging and dropping; automatically generate business models; discover anomalies, outliers and hidden patterns in their data; and understand data dependencies and the impact of changes.
AutoML UI: The feature automates time-intensive steps in the creation of machine learning models and 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.
Machine Learning for Python: Data scientists and other Python users can now use Python to apply machine learning on their data warehouse data.
Machine Learning Services: Using the Autonomous Data Warehouse, DevOps and data science teams can deploy and manage native in-database models and ONNX-format classification and regression models outside the product, and can also invoke cognitive text analytics.
Property Graph Support: Graphs help to model and analyse relationships between entities. Users can now create graphs within their data warehouse, query graphs using PGQL (property graph query language) and analyse graphs with over 60 in-memory graph analytics algorithms.
Graph Studio UI: It helps 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.
Access to Data Lakes: It brings three new data lake capabilities such as easy querying of data in Oracle Big Data Service (Hadoop); integration with OCI Data Catalogue to simplify and automate data discovery in object storage; and scale-out processing to accelerate queries of large data sets in object storage. This extends the data warehouse’s ability to query data in Oracle Cloud Infrastructure (OCI) Object Storage and all popular cloud object stores.