Analysts need an efficient way to consolidate data from multiple financial systems and other data sources into a trusted, maintainable, and query-optimized source.

Typically data mart projects struggle to optimize data from multiple sources which makes it very difficult to effectively analyze the data and generate actionable insights.

Autonomous Data Warehouse makes it easy to resolve these key challenges...

Human resource data is often distributed in multiple systems across the enterprise and can't easily be integrated and analyzed to produce actionable insights.

This makes it very difficult to capture raw application data, enrich it with data from other sources and produce actionable and predictive insights.

Autonomous Data Warehouse makes it easy to resolve these key challenges......

A modern data warehouse collects data from a wide variety of sources, both internal or external. The data is usually structured, often from relational databases, but it can be unstructured too pulled from "big data" sources such as Internet of Things devices etc.

Autonomous Data Warehouse makes it easy to integrate and secure data from many sources and then generate richer, smarter, faster business insights...

This architecture uses an autonomous database (which can be an Oracle Autonomous Transaction Processing database or an Oracle Autonomous Data Warehouse) provisioned in a private subnet; that is with a private endpoint.

This is a flexible architecture that can support multiple scenarios based on Oracle Machine Learning in Autonomous Data Warehouse. In addition to Autonomous Data Warehouse, it includes Data Catalog and Oracle Analytics Cloud along with three Oracle Cloud Infrastructure Compute instances.

This reference architecture shows how you can use a serverless function to automate the process of extracting data from files generated by various databases or applications and loading the data into a data warehouse for analysis.