We all aim for visually appealing interfaces and accurate data in our reports. However, the reality is that sometimes our application data originates from sources beyond our control. It's risky to assume that this data is clean and trustworthy. When users input data into a web form, there's always the potential for it to be malicious or improperly formatted. ⚠️ Never trust user input! This is why developers should never blindly trust user input. Instead, they must validate and sanitise it to ensure it aligns with expected criteria and is safe for use within the application. Failure to adher ..read more

We’ve recently been focusing on how data is presented to various audiences using different methods. The audiences can vary greatly by subject knowledge and can be thought of in terms of: Subject matter experts: These individuals have a deep understanding of the topic and want to explore the data to generate new insights Subject data users: This group requires data to accessible and understandable with the aim of staying informed and making data driven decisions Knowledge seekers: These individuals want to understand the subject in more detail to either learn something new or change their cur ..read more

For data engineers working in the cloud, Oracle Cloud Infrastructure (OCI) offers a suite of powerful functions designed to simplify data manipulation and analysis. Amongst these are three functions that stand out for their ability to handle structured data in different formats: json_table, xml_table, and the lesser-known unnest. Let us take a look into each of these functions and explore how they can aid in any cloud-based data workflows. json_table: JSON to Relational Tables The json_table function allows you to convert JSON data into a relational table format by extracting specified JSON d ..read more

Data type hints in Python, introduced in PEP 484 and enhanced in subsequent PEPs, provide a way to specify the expected types of variables, function parameters, and return values. While Python remains dynamically typed, type hints allow developers to add optional static typing to their code for documentation, readability, and tooling support. Here’s an overview of how data type hints work in Python: Type Hinting for Variables: # Specify the expected type of a variable name: str = "Jon" age: int = 30 price: float = 12.5 is_valid: bool = True Type Annotations for Function Parameters and Re ..read more

I have recently been working on an Oracle APEX upgrade project. As part of this upgrade, we undertook a migration that involved transitioning from the PL/SQL Gateway to Oracle REST Data Services (ORDS). Given the time gap since I last worked with on-premises systems (a non-cloud environment, if you will), I've been actively researching the optimal methods for initiating and halting ORDS. 1️⃣ The proper way If you deploy ORDS using a package manager, as well described in thatjeffsmith's blog here, the stop and start functionality should will already be available for you to enjoy. In this scena ..read more

I have recently been working on an Oracle APEX upgrade project. As part of this upgrade, we undertook a migration that involved transitioning from the PL/SQL Gateway to Oracle REST Data Services (ORDS). Given the time gap since I last worked with on-premises systems (a non-cloud environment, if you will), I've been actively researching the optimal methods for initiating and halting ORDS. 1️⃣ The proper way If you deploy ORDS using a package manager, as well described in thatjeffsmith's blog here, the stop and start functionality should will already be available for you to enjoy. In this scena ..read more

In a recent blog post we gave an overview of Retrieval Augmented Generation (RAG) as a way to leverage the natural language querying capabilities of Large Language Models (LLMs) on private or proprietary stores of unstructured data. A major use case for RAG is in the creation of chatbots which allow users to have a conversation with their documents, avoiding the potentially time-consuming task of searching through hundreds or thousands of files for a piece of information. As a demonstrator for this, Rittman Mead have used python and RAG to create a chatbot which searches a vector da ..read more

Oracle recently announced the general availability of Oracle Analytics Server 2024. While it introduces 100+ new capabilities, this article is focused on the top 10 ones according to my personal opinion. If you are interested in a comprehensive list of all the new features and enhancements please refer to the official documentation. 10. Navigation Menu The Home page features a new Navigation Menu, offering a convenient pathway to shared workbooks and dashboards. Figure 1. The new Navigation Menu9. Watchlists Watchlists allow consumer users to group together visualizations from one or mor ..read more

We are hosting a live webinar focused on Oracle Analytics Cloud. Discover AI and ML for OAC and see Data Science in Action. Our data science team recently collaborated with a higher education institution to create machine learning models that assess the likelihood of students failing to enrol at the start of the term. The outcomes, presented through an interactive dashboard in Oracle Data Visualiser, enable the admissions office to visualise and monitor these predictions dynamically. As a result of this project, the institution can intervene early with targeted support for these students, to ..read more

In today's fast-paced business environment, the need for rapid application development is more pressing than ever. The use of low-code and no-code tools in making new software is growing fast in the tech world. Gartner predicts that by 2025, about 70% of new apps made by companies will use these technologies. The purpose of this blog post is to offer a detailed comparison between Oracle APEX and Microsoft Power Apps, equipping you with the insights necessary to make a knowledgeable decision. Comparing Oracle APEX and Microsoft Power Apps is not a straightforward task, as they serve different ..read more