Book : Optimizing Databricks Workloads Harness The Power Of.

-Titulo Original : Optimizing Databricks Workloads: Harness The Power Of Apache Spark In Azure And Maximize The Performance Of Modern Big Data Workloads

-Fabricante :

Packt Publishing

-Descripcion Original:

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 Review I thoroughly enjoyed reading this book. This is probably the best technical book on how to optimize & performance tune your Azure Databricks workloads out there today. Having have had the opportunity of very closely collaborating with the authors on multiple Azure Databricks projects in past 2 years, I am supremely confident of the content, presentation and optimization and performance tuning strategies mentioned in the book, and that it will assist you - whether a data engineer, data scientist, data analyst and/or a big data architect - in becoming a master of Azure Databricks in your profession. This is a must-have in a big data connoisseur’s library. -Subhasish Ghosh, Cloud Solution Architect - Data & Analytics, Enterprise Commercial - US, Global Account - Customer Success Unit (CSU) team, Microsoft Corporation 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