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Optimizing Databricks Workloads: Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
Accelerate computations and make the most of your data effectively and efficiently on Databricks
Key FeaturesUnderstand 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 DescriptionDatabricks 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 learnGet 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 forThis 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 ContentsDiscovering DatabricksBatch and Real-Time Processing in DatabricksLearning about Machine Learning and Graph Processing in DatabricksManaging Spark ClustersBig Data AnalyticsDatabricks Delta LakeSpark CoreCase Studies
Key FeaturesUnderstand 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 DescriptionDatabricks 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 learnGet 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 forThis 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 ContentsDiscovering DatabricksBatch and Real-Time Processing in DatabricksLearning about Machine Learning and Graph Processing in DatabricksManaging Spark ClustersBig Data AnalyticsDatabricks Delta LakeSpark CoreCase Studies
230 pages, Kindle Edition
Published December 24, 2021
3 people are currently reading
2 people want to read
About the author
Anirudh Kala
9 books3 followersAnirudh Kala is a psychiatrist by profession and Clinical Director of Mind Plus, Ludhiana in Punjab. The latter is an intermediate stay facility for treatment of psychiatric illnesses and substance use disorders. He was a member of the expert group tasked by Government of India with writing Mental Health Policy for the country, which was released in 2014.
He also writes fiction and has published several short stories. An anthology of interconnected stories, Unsafe Asylum: Stories of Partition and Madness was published in 2018 by Speaking Tiger books. Two and a Half Rivers is a novel based on a young Dalit couple caught in the vortex of terrorism in Punjab and is his second work of fiction. A non-fiction book Most of What You Know About Addiction is Not True with focus on addictions in Punjab will be released early next year.
He has been instrumental in forging cross border links between two sides of Punjab and is founder of “Indo-Pak Punjab Psychiatric Society.”
He is fond of Urdu poetry, travelling and semi-classical music.
He also writes fiction and has published several short stories. An anthology of interconnected stories, Unsafe Asylum: Stories of Partition and Madness was published in 2018 by Speaking Tiger books. Two and a Half Rivers is a novel based on a young Dalit couple caught in the vortex of terrorism in Punjab and is his second work of fiction. A non-fiction book Most of What You Know About Addiction is Not True with focus on addictions in Punjab will be released early next year.
He has been instrumental in forging cross border links between two sides of Punjab and is founder of “Indo-Pak Punjab Psychiatric Society.”
He is fond of Urdu poetry, travelling and semi-classical music.
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Displaying 1 of 1 review
September 9, 2024
Excellent book. Clear and precise.
I recommend this book for beginners or intermediate users, as it not only covers the basics, but tries to go deeper into theory that not everyone knows even if they already use Databricks.
I recommend this book for beginners or intermediate users, as it not only covers the basics, but tries to go deeper into theory that not everyone knows even if they already use Databricks.
Displaying 1 of 1 review