Oracle Autonomous Data Warehouse

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• SaaS - History. Philosophy. Drivers of development
• Database Management System (DBMS)
• Data management
• Data mining Intelligent data analysis

2021: Built-in Data Processing Tools

On March 25, 2021, the company Oracle announced a set of changes to Oracle Autonomous Data Warehouse, offline. cloudy storage of data Cloud storage data has evolved from a complex ecosystem of products, tools, and tasks that require extensive knowledge of technology, time, and investment into an intuitive point-and-click application SaaS that can be used by data-, analysts data-sainters, and technicians. business processes Oracle Autonomous Data Warehouse enables companies to achieve planned results faster and get analytics data. With the ability to "zero administration," Oracle Autonomous Data Warehouse enables small, medium, and large businesses to reduce costs while benefiting from data.

Oracle Autonomous Data Warehouse

According to the company, the latest updates in Oracle Autonomous Data Warehouse provide the business with a single data platform that allows you to accumulate data from any source, transform, store and manage data for a variety of analytical work tasks, from creating industry management systems, to enterprise data warehouses and lakes. Simple integrated tools provide an intuitive drag & drop interface that simplifies loading, conversion, data cleansing for analysts, as well as the ability to automatically create business models and find patterns for generating analytics.

The release adds self-service tools for analysts and data sainters, allowing you to easily create data arrays, create AutoML-based machine learning models, and deploy models. To enable developers to create data-driven applications, Oracle offers Oracle APEX (Application Express) Application Development, a low-code application development tool built directly into the database, as well as RESTful services that optimize the interaction of any modern application with the data warehouse. Unlike single-purpose databases from other cloud providers, Oracle Autonomous Data Warehouse supports multi-model, multi-tasking, and multi-user requirements - all within a single, state-of-the-art converged database engine, including JSON support, operational, analytics, graphics, machine learning, and blockchain services.

Updates for Oracle Autonomous Data Warehouse, aimed at democratizing all aspects of analytics and machine learning, effectively eliminating the need SQL for users to learn. Oracle has taken the path of replacing programming the drag & drop and AutoML user interface to create and test machine learning models, which allows users specializing in business operations to do their own work analytics without involving experts in, IT administrators databases or system administrators. All this is possible inside the Oracle converged database, which gives users access to all models and data types within a single database. said Holger Muller, vice president and chief analyst at Constellation Research

In addition to a wide range of capabilities that make it easier for analysts, data sainters, and industry developers to leverage the capabilities of the first and only offline cloud storage in the market segment, the release provides features that enable deeper analytics and closer integration with the data lake. In the list of key features:

• Built-in data processing tools - Business Intelligence has a simple, self-service environment to load data and provide it to the extended team for collaboration. They can load and convert data from their computer or cloud using drag & drop interface. They can then automatically create business models; quickly detect anomalies, emissions and hidden patterns in their data; Understand data dependencies and the impact of changes.
• Oracle Machine Learning AutoML: Automating the time-consuming steps of creating machine learning models, AutoML optimize the productivity of data specialists, optimize model accuracy, and allow even non-specialists to access the use of machine learning. AutoML can be called via Python or via the AutoML user interface without using code.
• Oracle Machine Learning for Python - Data specialists and other Python users can now use Python to apply machine learning to data in their storage, fully leveraging the high-performance parallel capabilities of Oracle Autonomous Data Warehouse.
• Oracle Machine Learning Services: DevOps and data site groups can now deploy and manage their own models in the database, as well as classification and regression models in ONNX format outside Oracle Autonomous Data Warehouse, as well as use cognitive text analytics. Application developers have received easy-to-integrate REST endpoints for all features.
• Graph support: graphs help you model and analyze relationships between objects (for example, a social network graph). Users can now create graphs in their data store, query graphs using PGQL (property graph query language), and analyze graphs using more than 60 in-memory algorithms.
• The Graph Studio: Graph Studio user interface is based on Oracle Autonomous Data Warehouse graph capabilities and optimizes graph analysis for beginners. The interface combines automatic modeling, integrated visualization, and off-the-shelf workflows for different uses.