Posts by Hyukjin Kwon

We are excited to announce the availability of Apache Spark™ 3.2 on Databricks as part of Databricks Runtime 10.0. We want to thank the Apache Spark community for their valuable contributions to the Spark 3.2 release. The number of monthly maven downloads of Spark has rapidly increased to 20 million. The year-over-year growth rate represents...

We’re thrilled to announce that the pandas API will be part of the upcoming Apache Spark™ 3.2 release. pandas is a powerful, flexible library and has grown rapidly to become one of the standard data science libraries. Now pandas users will be able to leverage the pandas API on their existing Spark clusters. A few...

Koalas is a data science library that implements the pandas APIs on top of Apache Spark so data scientists can use their favorite APIs on datasets of all sizes. This blog post compares the performance of Dask’s implementation of the pandas API and Koalas on PySpark. Using a repeatable benchmark, we have found that Koalas...

We are excited to announce the availability of Apache Spark 3.1 on Databricks as part of Databricks Runtime 8.0. We want to thank the Apache Spark™ community for all their valuable contributions to the Spark 3.1 release. Continuing with the objectives to make Spark faster, easier and smarter, Spark 3.1 extends its scope with the...

Controlling the environment of an application is often challenging in a distributed computing environment - it is difficult to ensure all nodes have the desired environment to execute, it may be tricky to know where the user’s code is actually running, and so on. Apache Spark™ provides several standard ways to manage dependencies across the...

At Databricks, we strive to provide a world-class development experience for data scientists and engineers, and new features are constantly getting added to our notebooks to improve our users’ productivity.  We are especially excited about the latest of these features, a new autocomplete experience for Python notebooks (powered by the Jedi library ) and new...

Apache Spark™ has reached its 10th anniversary with Apache Spark 3.0 which has many significant improvements and new features including but not limited to type hint support in pandas UDF, better error handling in UDFs, and Spark SQL adaptive query execution. It has grown to be one of the most successful open-source projects as the...

Koalas is an open source project which provides a drop-in replacement for pandas, enabling efficient scaling out to hundreds of worker nodes for everyday data science and machine learning. After over one year of development since it was first introduced last year, Koalas 1.0 was released. pandas is a Python package commonly used among data...

Apache Spark is a very popular tool for processing structured and unstructured data. When it comes to processing structured data, it supports many basic data types, like integer, long, double, string, etc. Spark also supports more complex data types, like the Date and Timestamp, which are often difficult for developers to understand. In this blog...