{"id":5695,"date":"2019-12-20T21:04:05","date_gmt":"2019-12-20T20:04:05","guid":{"rendered":"https:\/\/blog.jetbrains.com\/kotlin\/?p=7665"},"modified":"2023-08-08T16:08:37","modified_gmt":"2023-08-08T15:08:37","slug":"making-kotlin-ready-for-data-science","status":"publish","type":"kotlin","link":"https:\/\/blog.jetbrains.com\/ko\/kotlin\/2019\/12\/making-kotlin-ready-for-data-science","title":{"rendered":"Making Kotlin Ready for Data Science"},"content":{"rendered":"

This year at KotlinConf 2019, Roman Belov gave an overview on Kotlin’s approach to data science. Now that the talk is available<\/a> for everyone to see, we decided to recap it and share a bit more on the current state of Kotlin tools and libraries for data science.<\/p>\n

https:\/\/www.youtube.com\/watch?v=APnyDVye4JA<\/a><\/p>\n

How does Kotlin fit data science? Following the need to analyze large amounts of data, the last few years has brought a true renaissance to the data science discipline. All this renaissance of data science couldn’t be possible without proper tools. Before, you needed a programming language designed specifically for data science, but today you can already do it with general-purpose languages. Of course this requires general-purpose languages to make the right design decisions, not to mention getting the community to help in. All this made certain languages, such as Python, more popular for data science than others.<\/p>\n

With the concept of Kotlin Multiplatform, Kotlin aims to replicate its developer experience and extend its interoperability to other platforms as well. The major qualities of Kotlin by design include conciseness, safety, and interoperability. These fundamental language traits make it a great tool for a wide variety of tasks and platforms. Data science is certainly one of these tasks.<\/p>\n

The great news is that the community has already begun adopting Kotlin for data science, and this adoption is happening at a fast pace. The brief report below outlines how ready Kotlin is for data science, including the Kotlin libraries and Kotlin tools for data science.<\/p>\n

<\/p>\n

Jupyter<\/h3>\n

First and foremost, thanks to their interactivity, Jupyter notebooks<\/a> are very convenient for transforming, visualizing, and presenting data. With the extensibility and the open-source nature of Jupyter, it has turned into a large ecosystem around data science and was integrated into tons of other solutions related to data. Among them is the Kotlin kernel<\/a> for Jupyter notebooks. With this kernel, you can write and run Kotlin code in Jupyter notebooks and use third-party data science frameworks written in Java and Kotlin.<\/p>\n

An example of a reproducible Kotlin Jupyter notebook can be found in this repo<\/a>. To quickly play with a Kotlin notebook, you can launch it on Binder<\/a> (please note the environment will normally take a minute to set up).<\/p>\n

Apache Zeppelin<\/h3>\n

Due to the strong support for Spark and Scala, Apache Zeppelin<\/a> is very popular among data engineers. Similar to Jupyter, Zeppelin has a plugin API (called Interpreters) to extend its core with support for other tools and languages. Currently, the latest release of Zeppelin (0.8.2) doesn\u2019t come with a bundled Kotlin interpreter. But anyway, it is available in the master branch of Zeppelin. To learn how to deploy Zeppelin with Kotlin support in a Spark cluster, see these instructions<\/a>.<\/p>\n

Apache Spark<\/h3>\n

Since Spark has a robust Java API, you can already use Kotlin to work with the Spark Java API from both Jupyter and Zeppelin without any problems. However we\u2019re working on improving this integration by adding full support for Kotlin classes with Spark\u2019s Dataset API. Support for Kotlin with Spark’s shell is also in progress.<\/p>\n

Libraries<\/h3>\n

Using Kotlin for data science alone, without libraries, makes little sense. Luckily, thanks to the recent efforts of the community, there\u2019s already a number of nice Kotlin libraries that you can use right away.<\/p>\n

Here are some of the most useful libraries:<\/p>\n