This blog post explains the in-memory data cluster reloading improvements of MySQL HeatWave version 9.4.2

An overview of Library Feature in the MySQL HeatWave managed cloud service and elsewhere.

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 …

Discover how to tame big tables using LIMIT and OFFSET in MySQL! This post makes pagination easy for newcomers, using friendly library metaphors, step-by-step SQL examples, and practical tips to help you view your data one page at a time.

This blog post introduces the MySQL Autopilot Indexing feature.

We are excited to announce the release of Natural Language to SQL (NL2SQL) capability for MySQL (with the AI option) and MySQL HeatWave cloud service. Natural language interface is essential for modern data platforms, allowing users to explore information more quickly.

Agentic AI represents a significant leap forward from simple chatbots. Instead of just responding to a single question, an AI “agent” can proactively pursue a goal. It can decompose a complex request into a logical series of steps, choose the right tools for each step—like querying a database or running a calculation—and then execute that […]

This article showcases the power of combining the LangChain framework with an LLM hosted on MySQL HeatWave GenAI. Our goal is to create an agent that can answer the question, "What is the current weather in Fahrenheit?". To do this, the agent must first fetch the weather (which is in Celsius) and then convert it to Fahrenheit, a classic multi-step task. This agent will demonstrate how a Large Language Model (LLM) can use tools and a logical sequence of steps to solve a problem.

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the capabilities of Large Language Models (LLMs) for enterprise data. By grounding the model in information from your vector store/knowledge base, RAG helps produce more accurate, relevant, and trustworthy answers. MySQL HeatWave is a fully managed MySQL database service that combines transactions, analytics, machine learning, and […]

This post will guide you through building a simple RAG workflow using the popular LangChain framework, powered entirely by the in-database AI capabilities of MySQL HeatWave GenAI. We will ingest a public blog post, index it in the HeatWave Vector Store, and use a HeatWave-hosted LLM to answer questions based on its content following the LangChain guide to creating vector stores and using them for RAG.