Modern AI systems increasingly rely on multimodal data: text, images, documents, audio, and video. Among these modalities, image understanding has become one of the most important capabilities for AI-powered applications.
Traditionally, implementing these capabilities required specialized computer vision infrastructure, external vector databases, custom ML pipelines, and multiple frameworks.
With MySQL HeatWave GenAI, many of these capabilities can now be implemented directly inside SQL workflows using built-in AI routines.
In this article, we will build the foundations of a Vision Model Evaluation Assistant using MySQL HeatWave GenAI. The objective is not to create another image classifier, but rather a semantic image understanding platform.

The post Building an AI Vision Search Engine with MySQL …

The starter kit deploys a MySQL HeatWave DB System on OCI and enables the MySQL REST Service automatically: The REST Service enables us to provide access to data without requiring SQL. It also provides access to some Gen AI functionalities available in MySQL HeatWave. Adding data to MRS using Visual Studio Code To be able […]

In part 4 of our series on the OCI Hackathon Starter Kit, we saw how to connect to the deployed MySQL HeatWave instance from our clients (MySQL Shell, MySQL Shell for VS Code, and Cloud Shell). In this post, we will see how to connect from an application using a connector. We will cover connections […]

Let’s now see how we can connect to our MySQL HeatWave DB System, which was deployed with the OCI Hackathon Starter Kit in part 1. We have multiple possibilities to connect to the DB System, and we will use three of them: MySQL Shell in the command line MySQL Shell is already installed on the […]

Bridging natural language processing with semi-structured data brings both opportunity and complexity.

MySQL HeatWave GenAI’s NL2SQL feature shows how natural language can simplify data interaction — even for JSON documents. Yet, because JSON embeds both data and metadata within a single column, LLMs may struggle without explicit schema cues.

By creating well-structured views that reveal JSON’s internal organization, you can transform unstructured data into a relational format the model understands — improving both query accuracy and overall usability.

This approach highlights how MySQL HeatWave continues to evolve as a powerful engine for intelligent, natural language–driven analytics.

The post Querying the Unstructured: Natural Language to SQL for JSON Data first appeared …

Having explored the innovative MySQL HeatWave technology that converts Natural Language into SQL (Ask Your Database Anything: Natural Language to SQL in MySQL HeatWave), our next article in this series, dives into a practical use case demonstrating how an AI DBA Assistant can significantly simplify your query generation workflow. In MySQL, there are 3 specialized […]

The post Let Your AI DBA Assistant Write Your MySQL Queries first appeared on Data Daz (dasini.net) - Data Systems, AI, and Real-World Insights.

As databases grow, DBAs and developers often face a tough choice:
+ Keep historical data in production tables and risk slow queries
+ Archive it and lose fast access

With HeatWave, you don’t have to choose. You can archive old partitions, keep recent data in production, and still run lightning-fast queries across all your data.

The post Archive Smarter, Query Faster: Unlocking MySQL Performance with HeatWave first appeared on Data Daz (dasini.net) - Data Systems, AI, and Real-World Insights.

Have you already tried to upgrade the MySQL version of your MySQL HeatWave instance in OCI that is deployed with Terraform? When you tried, you realized, I hope you didn’t turn off backups, that the instance is destroyed and recreated new! This is our current MySQL HeatWave DB System deployed using Terrafrom: And this is […]

HeatWave MySQL Database Audit brings powerful enterprise-grade auditing capabilities to the cloud, allowing organizations to monitor and track database activity for security, compliance, and performance optimization. With features like customizable filters, real-time monitoring, minimal overhead, and seamless integration with MySQL tools, it enables administrators to log critical operations, detect threats, and maintain detailed records for regulatory requirements. The audit system is easy to set up, supports granular activity tracking, and provides actionable insights directly through the SQL interface.

The post HeatWave MySQL Database Audit first appeared on Data Daz (dasini.net) - Data Systems, AI, and Real-World Insights.

Efficiently loading CSV data into your MySQL environment is a crucial step for many analytical workloads, especially when dealing with the substantial datasets common in today's data-driven world. Both MySQL Shell's parallel import utility and HeatWave's Auto Parallel Load feature offer robust and effective solutions, each catering to slightly different needs and environments.

MySQL Shell's util.importTable provides a flexible and powerful way to import data from various sources, including local disks and OCI Object Storage, into your standard MySQL tables. It's a developer-friendly tool that offers granular control over the import process.

For those leveraging the analytical power of MySQL HeatWave, the sys.heatwave_load stored procedure, particularly with its Auto Parallel Load capabilities, simplifies the often complex task of getting data into the in-memory columnar store.

The post …