Application development with Oracle Database

Build a mobile food delivery app with a data-centric architecture pattern using Kubernetes engine, Docker containers, and Oracle Autonomous Database. Modern AppDev is synonymous with data-driven microservices generating operational data and code events, and it incorporates artificial intelligence (AI) and/or machine learning (ML). A converged data platform on Oracle Cloud Infrastructure (OCI) simplifies the creation of new microservices to handle a variety of events and data with tables, JSON, graph, and spatial data types. The GrabDish app example shows how a complete microservices platform can be instantiated automatically using terraform scripts, simplified DevOps with Kubernetes, and sample microservices code.

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e-shop is a web-based, ecommerce retail application example, developed to demonstrate Oracle Sharding, a hyperscale globally distributed converged database with OLTP and analytics in a massively parallel processing (MPP) architecture. Multiple technologies and products are usually required to develop an application such as this. For example, you would need a JSON database, a text index application, a relational database, and an analytics engine—all of which makes it difficult to query data across multiple data stores. This example uses Oracle Sharding (with three shards) with different types of data —including structured and unstructured data, relational, JSON, and text—stored in a single database platform.

This example app includes multiple capabilities, such as the simple Oracle document access (SODA) API and text search for JSON, joins, transactions, and ACID properties for relational queries, fuzzy match, type ahead, free-form text search, and sentiment.

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Make specialized offers to at-risk customers using Autonomous Database.

Modern data platforms require analytics across storage engines (relational, object storage, and others) and data types. Oracle Autonomous Database captures tabular, spatial, graph, and JSON data and includes specialized algorithms for each of these data types. Oracle MovieStream—a fictitious online movie streaming company—is used to illustrate how different types of sophisticated analytics can be applied to both structured and unstructured data. You can access data from a data lake, unlock and discover movie viewing insights from tabular and JSON data, use machine learning to predict churn, localize offers using spatial analytics, and apply graph algorithms to recommend movies based on derived communities.

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Build and deploy a personal productivity application using cloud native services, such as Oracle Autonomous Database, Oracle Container Registry, Kubernetes clusters, API gateway, and Helidon.

This application makes REST calls to a Java backend using the Helidon microservices framework. The application is containerized using Docker, deployed in the Container Registry, and managed by Kubernetes. The Java code implements the REST APIs and connects to the Autonomous Database to persist changes.

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A shopping cart app allows customers to select, review, and purchase a product or service from an online store at any time.

This sample application creates a shopping cart from scratch using Oracle Autonomous Database and Oracle APEX. First, a sample dataset is imported, and then an app is built with pages, such as dashboard, products, and multiple reports. You can then add pages to manage the shopping cart.

The application allows online store administrators to manage products, customers, and stores. Customers can explore, add products to the cart, and eventually proceed to checkout. This method can be applied to other use cases, building a starter application in 60 minutes with customer features and administrator features to securely manage changes to the site.

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Oracle Machine Learning is a powerful solution for analyzing data and applying it to the most valuable data in your organization—the existing enterprise data in your Oracle Database. This example shows how a data professional can build a machine learning–driven insurance app with APEX, starting with data preparation and finishing up with the successful deployment of a model.

The sample insurance application also addresses DevOps best practices for model deployment in real applications so developers have simple access to powerful AI/ML.

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