dasini.net – Diary of a MySQL expert

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.

While HeatWave GenAI excels at generating English text, its capabilities extend to translation as well. Let’s explore how we can effectively translate English content into French using this powerful tool.

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.

– Read Replicas with Load Balancer
– Replication Filters
– Replication Sources Without GTIDs
– MySQL HeatWave for AWS

This presentation is just a summary of new features in MySQL HeatWave.

For a more thorough and exhaustive view please read the following:

– https://docs.oracle.com/en-us/iaas/releasenotes/services/mysql-database/

– https://dev.mysql.com/doc/relnotes/heatwave/en/

During the last 20 years MySQL has democratized the usage of transactional databases.
Now with MySQL HeatWave we are in the process of democratizing Analytics and Machine Learning.

With MySQL HeatWave,
valorizing your data has never been so easy!

In this article I will show you how easy it is to use the Machine Learning capabilities of MySQL HeatWave. We will use the famous Iris dataset and we’re going to build, train, deploy and explain a machine learning model within MySQL HeatWave using Apache Zeppelin.

In this article I will show you how to properly configure Apache Zeppelin in order to take advantage of the Analytics and Machine Learning capabilities of MySQL HeatWave the MySQL in-memory query accelerator.

In this article I will show you how to properly configure Apache Superset in order to take advantage of a high performance, in-memory query accelerator: MySQL HeatWave.

Yet another customer wanted to migrate from MariaDB to MySQL. So I decided to write down the overall data migration process.

Indeed each migration are a unique story so I won’t provide you a “How to migrate”…
The idea is to highlight the general path and help you to avoid common pitfalls and thus help you save some precious time.