Aleksei Zinovev
JetBrains, TeamLead in Kotlin for Data Science
Tempered in the crucible of Apache Hadoop and Apache Spark, the last few years, Alex is working on Machine Learning frameworks for JVM programming languages (Java, Scala, and Kotlin).
In 2017–2020 he worked on an ML module for a distributed in-memory database, Apache Ignite
2020 was the year when Alexey created and released the new Deep Learning framework in Kotlin (Kotlin DL https://github.com/JetBrains/KotlinDL)
Since 2023, Alexey has been leading the development and research in the development of data analysis tools in Kotlin.
Unlocking SQL Databases with Kotlin Data Analytics: A Practical Exploration
Throughout the session, I will cover a range of key use cases, demonstrating the versatile capabilities of Kotlin DataFrame for seamless interaction with SQL databases (MariaDB or PostgreSQL, for example).
We will start from the basics of exploring and understanding the structure of SQL databases with Kotlin DataFrame metadata reading methods.
After that, we will learn how to effortlessly extract data from tables, execute custom SQL queries, or utilize existing ResultSet objects (for JDBC fans) using Kotlin DataFrame, streamlining the data retrieval process.
In the third part, I will demonstrate to you the dynamic generation of classes and schemas on the fly for the tables or results of SQL queries, empowering you to construct flexible data processing pipelines in Kotlin DataFrame with ease.
It's funny to see how your data from an SQL database could be quickly drawn with charts, bar plots, and heat maps. Kandy plotting library will help us better understand the nature of data stored in SQL databases.
In the end, we will see how it is to join together the data from multiple databases.
Join me on this comprehensive journey where we unravel the intricacies of SQL databases using Kotlin DataFrame, empowering you with the skills to navigate, retrieve, visualize, and process data efficiently.