Benchmark: Koalas (PySpark) and Dask
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...
Introducing Apache Spark™ 3.1
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...
How to Manage Python Dependencies in PySpark
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...
Python Autocomplete Improvements for Databricks Notebooks
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...
An Update on Project Zen: Improving Apache Spark for Python Users
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...
Interoperability between Koalas and Apache Spark
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...
A Comprehensive Look at Dates and Timestamps in Apache Spark™ 3.0
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...
Introducing Koalas 1.0
Koalas was first introduced last year to provide data scientists using pandas with a way to scale their existing big data workloads by running them on Apache SparkTM without significantly modifying their code. Today at Spark + AI Summit 2020, we announced the release of Koalas 1.0. It now implements the most commonly used pandas...
Vectorized R I/O in Upcoming Apache Spark 3.0
R is one of the most popular computer languages in data science, specifically dedicated to statistical analysis with a number of extensions, such as RStudio addins and other R packages, for data processing and machine learning tasks. Moreover, it enables data scientists to easily visualize their data set. By using SparkR in Apache SparkTM, R...
New Pandas UDFs and Python Type Hints in the Upcoming Release of Apache Spark 3.0
Pandas user-defined functions (UDFs) are one of the most significant enhancements in Apache SparkTM for data science. They bring many benefits, such as enabling users to use Pandas APIs and improving performance. However, Pandas UDFs have evolved organically over time, which has led to some inconsistencies and is creating confusion among users. The full release...
