Optimizing Databricks Workloads : Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
Language: English
Published by Packt Publishing, 2021
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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US$ 106.01
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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…
Seller Inventory # 9781801819077
- Title
- Optimizing Databricks Workloads : Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
- Author
- Anshul Bhatnagar
- Publisher
- Packt Publishing
- Publication year
- 2021
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1801819076
- ISBN 13
- 9781801819077
- Item weight
- 438 grams
- Dimensions
- 235x191x13 mm
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
Book 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 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
Who 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.
Table of Contents
- Discovering Databricks
- Batch and Real-Time Processing in Databricks
- Learning about Machine Learning and Graph Processing in Databricks
- Managing Spark Clusters
- Big Data Analytics
- Databricks Delta Lake
- Spark Core
- Case Studies
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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