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Packt Optimizing Databricks Workloads: Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads

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Publisher	Packt Publishing
ISBN 13	9781801819077
ISBN 10	1801819076
Author	Anirudh Kala
Book Format	Paperback

Language	English
Book Description	Accelerate computations and make the most of your data effectively and efficiently on DatabricksKey Features: Understand Spark optimizations for big data workloads and maximizing performanceBuild efficient big data engineering pipelines with Databricks and Delta LakeEfficiently manage Spark clusters for big data processingBook Description: 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.What You Will Learn: Get to grips with Spark fundamentals and the Databricks platformProcess big data using the Spark DataFrame API with Delta LakeAnalyze data using graph processing in DatabricksUse MLflow to manage machine learning life cycles in DatabricksFind out how to choose the right cluster configuration for your workloadsExplore file compaction and clustering methods to tune Delta tablesDiscover advanced optimization techniques to speed up Spark jobsWho this book is for: This book is for data engineers, data scientists, and cloud architects who have working knowledge of Spark/Databricks and some basic understanding of data engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial.
About the Author	Anirudh Kala is an expert in machine learning techniques, artificial intelligence, and natural language processing. He has helped multiple organizations to run their large-scale data warehouses with quantitative research, natural language generation, data science exploration, and big data implementation. He has worked in every aspect of data analytics using the Azure data platform. Currently, he works as the director of Celebal Technologies, a data science boutique firm dedicated to large-scale analytics. Anirudh holds a computer engineering degree from the University of Rajasthan and his work history features the likes of IBM and ZS Associates.
Publication Date	24 December 2021
Number of Pages	230 pages

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