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

Chapter 3: Learning about Machine Learning and Graph Processing in Databricks

Databricks is ideal for productionalizing data science projects. It provides specialized runtimes for machine learning (ML) and integration with MLflow. MLflow is an open source project that helps to manage an end-to-end (E2E) ML life cycle. Databricks provides a managed version of MLflow as part of its complete offering.

Graph processing is yet another offering in Databricks. This is made available by GraphFrames, a Spark package that makes graph analysis accessible using DataFrames. We will look at examples for both ML and graph processing in this chapter.

The following topics are covered in this chapter:

  • Learning about ML components in Databricks
  • Practicing ML in Databricks
  • Learning about MLflow
  • Learning about graph analysis in Databricks