Book Image

Optimizing Databricks Workloads

By : Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi
Book Image

Optimizing Databricks Workloads

By: Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi

Overview of this book

Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud. In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains. By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently.
Table of Contents (13 chapters)
1
Section 1: Introduction to Azure Databricks
5
Section 2: Optimization Techniques
10
Section 3: Real-World Scenarios

Learning to differentiate Pandas and Koalas

The Pandas project is a very popular data transformation library in Python that is widely used for data analytics and data science purposes. Put simply, it's the bread and butter of data science for the majority of data scientists. But there are some limitations with the Pandas project. It is not really built for working with big data and distributed datasets. Pandas code, when executed in Databricks, only runs on the driver. This creates a performance bottleneck when the data size increases.

On the other hand, when data analysts and data scientists start working with Spark, they need to be using PySpark as an alternative. Due to this challenge, the creators of Databricks came up with another project and named it Koalas. This project has been built to allow data scientists working with Pandas to become productive with Apache Spark. It is nothing but a Pandas DataFrame API built on top of Apache Spark. Therefore, it leverages very...