Optimizing Databricks Workloads: Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
Author: Anirudh Kala ,Anshul Bhatnagar ,Sarthak Sarbahi(Author)
Publisher finelybook 出版社: Packt Publishing (December 24,2021)
Language 语言: English
Print Length 页数: 230 pages
ISBN-10: 1801819076
ISBN-13: 9781801819077
Book Description
By finelybook
Accelerate computations and make the most of your data effectively and efficiently on Databricks
Key Features
Understand Spark optimizations for big data workloads and maximizing performance
Build efficient big data engineering pipelines with Databricks and Delta Lake
Efficiently manage Spark clusters for big data processing
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.
Author: 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.
What you will learn
Get to grips with Spark fundamentals and the Databricks platform
Process big data using the Spark DataFrame API with Delta Lake
Analyze data using graph processing in Databricks
Use MLflow to manage machine learning life cycles in Databricks
Find out how to choose the right cluster configuration for your workloads
Explore file compaction and clustering methods to tune Delta tables
Discover advanced optimization techniques to speed up Spark jobs
