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Optimizing Databricks Workloads
Optimizing Databricks Workloads

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

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Profile Icon Anirudh Kala Profile Icon Bhatnagar Profile Icon Sarbahi
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£26.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (13 Ratings)
eBook Dec 2021 230 pages 1st Edition
eBook
£26.99
Paperback
£32.99
Subscription
Free Trial
Renews at £9.99p/m
Arrow left icon
Profile Icon Anirudh Kala Profile Icon Bhatnagar Profile Icon Sarbahi
Arrow right icon
£26.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (13 Ratings)
eBook Dec 2021 230 pages 1st Edition
eBook
£26.99
Paperback
£32.99
Subscription
Free Trial
Renews at £9.99p/m
eBook
£26.99
Paperback
£32.99
Subscription
Free Trial
Renews at £9.99p/m

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 24, 2021
Length: 230 pages
Edition : 1st
Language : English
ISBN-13 : 9781801811927
Vendor :
Databricks , Microsoft
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Languages :

What do you get with eBook?

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Product Details

Publication date : Dec 24, 2021
Length: 230 pages
Edition : 1st
Language : English
ISBN-13 : 9781801811927
Vendor :
Databricks , Microsoft
Category :
Languages :

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Frequently bought together


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Total £ 111.97
Azure Databricks Cookbook
£41.99
Data Engineering with Apache Spark, Delta Lake, and Lakehouse
£36.99
Optimizing Databricks Workloads
£32.99
Total £ 111.97 Stars icon
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Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(13 Ratings)
5 star 30.8%
4 star 61.5%
3 star 0%
2 star 0%
1 star 7.7%
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A Jan 17, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book gave me whole new prospective in addressing few of the challenges that I was facing. The authors have put a lot of thought into making this book useful to address real world challenges and it's well done. This book also provides detailed insights about the data lake and the spark.
Amazon Verified review Amazon
Kindle Customer Sep 09, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Emily Jan 07, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a data scientist building a full-stack data pipeline (extraction to analysis), Databricks is my “bread and butter” in terms of tools - I use SQL, Python, and Spark daily. As I write this, I worked through CHs 2, 4, and 7 (data processing topics) and am so glad I got this book. I just built an ELT pipeline for my Databricks workflow and this book was an excellent resource (especially since online documentation is somewhat limited and still growing). I am starting to work through CHs 5 and 3 (ML and analytics topics) as I build the 2nd half of the pipeline. So far it’s very helpful. I like this author’s style of presenting information so have recommended this book and their other books to both data scientists and data engineers.
Amazon Verified review Amazon
Subhasish Ghosh Jan 19, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Having read this book, must say, this is one of the best books authored on Azure Databricks in the market today. A few salient points:- Personally, there are very very few people whom I truly, deeply and sincerely admire & respect based on their combination of solid technical expertise, customer centricity + focus & ability to deliver complex projects under pressing timelines having worked across multiple projects across multiple continents, these past 15+ years. Anirudh Kala & Anshul Bhatnagar, both with Celebal Technologies, both of whom I have had the opportunity of working day & night on some supremely complex Azure projects past 2+ years - I can say, first-hand from my personal experience - are both probably the 2 best Technical Experts on Azure Databricks in India, and to write on this topic.- Secondly, I read through the eBook version of the book. It is straight to the point, matter of fact concepts explained, with code to execute wherein relevant.a) Core concepts including Delta Lake, core differences between parquet, avro & orc, Batch ETL process stages, Spark Structured Streaming WALs, MLflow, GraphFrames are well explained.b) Optimizing Databricks strategies and solutions are all excellent to save your precious DBUs (== dollars) and getting the best performance. Loved the section on 'column predicate pushdown' as an optimization technique.c) The chapter on Spark Core Performance Tuning is simply fantastic. Every Databricks Developer/Expert ought to know all these points by heart.- The chapter on Case Studies is excellent. Am very happy and proud to see that a few projects wherein I have had the opportunity of collaborating with the Authors on delivering some solid complex projects are mentioned therein... very proud of some of the work we have done in 2019-2021.- In summary, I strongly recommend getting this book if you're aspiring to or already a professional Data Engineer and/or Data Scientist and/or Data Analyst and/or Data Architects. Big Data is a truth today, and a solid grasp of Databricks, and how to optimize Spark workloads on Databricks for different use-cases efficiently, is a mandatory must.Once again, thank you to the authors for the wonderful effort, and to Packt, Priyanka Mhatre for inviting me to read the eBook and provide a review of the same. Am sure the Big Data community would love this Book.
Amazon Verified review Amazon
Rakesh Sundru Dec 27, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is super useful. Very well written.
Amazon Verified review Amazon
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