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

Practicing ML in Databricks

In this section, we will perform a simple ML experiment in Databricks using Spark ML. We will begin with exploratory data analysis (EDA) to understand the dataset. Following this, an ML experiment using the Decision tree algorithm will be performed. Decision tree algorithms are usually used for classification problems.

We will be using data from the DataSF project that was launched in 2009 and contains hundreds of datasets from the city of San Francisco. The dataset we will be using concerns San Francisco's fire department. The dataset contains data about all the calls made to the fire department and their responses.

We will divide this section into three phases, as follows:

  • Environment setup: Setting up a Spark cluster and getting the data
  • EDA: Analyzing the dataset by answering questions to enhance data understanding
  • ML: Using the Decision tree algorithm to address a classification problem

Environment setup

Let&apos...