dasini.net – Diary of a MySQL expert

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.

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 […]

Natural Language to SQL in MySQL HeatWave makes data accessible to everyone, not just SQL experts. By turning plain English into optimized queries, it removes barriers and speeds up insights. While best practices help ensure accuracy, the feature already shows how LLMs are transforming the way we work with data. With the NL_SQL routine, asking your database questions is as simple as asking a colleague.

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.

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.

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.

This tutorial explores HeatWave GenAI, a cloud service that simplifies interacting with unstructured data using natural language. It combines large language models, vector stores, and SQL queries to enable tasks like content generation, chatbot, and retrieval-augmented generation (RAG). The focus is on RAG and how HeatWave GenAI’s architecture helps users gain insights from their data.

This new AI tech, called generative AI (or GenAI), can dive deep into what people are saying and tell us if they’re feeling positive, negative, or neutral.
Let’s see how HeatWave GenAI, can help you to enhance your understanding of customer sentiment, improve decision-making, and drive business success.

Generative artificial intelligence (GenAI) is reshaping the content creation landscape. By training on vast datasets, these “intelligent” systems can produce new, human-quality content across a multitude of domains.

Oracle’s HeatWave GenAI (starting with version 9.0.1) is at the forefront of this revolution, offering an integrated platform that combines in-database large language models (LLMs), vector stores, and scale-out vector processing to streamline content generation.
This article explores how HeatWave GenAI is empowering businesses to produce high-quality content rapidly and effectively, making it an indispensable tool for industries demanding speed, accuracy, and security.

This article guides you through setting up replication between a HeatWave MySQL instance (source) and an on-premise standalone MySQL instance (replica).
It highlights key steps like creating a replication user, securing the connection, and using MySQL Shell utilities for data transfer.