Item #:
243608544
Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines
Item #:
243608544
UZS 923978
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What Stands Out
Seamless Integration
Easily connect Azure Databricks with various data sources, allowing users to streamline data workflows and enhance collaboration across data teams, maximizing productivity and efficiency.
Scalable Architecture
Utilize a scalable infrastructure that automatically adjusts resources based on workload demands, ensuring optimal performance for enterprise data pipelines and reducing operational costs.
Advanced Security
Benefit from robust security features, including data encryption and strict access controls, which protect sensitive information and comply with enterprise-level data governance and regulatory frameworks.
Product Details
- Quickly build and deploy massive data pipelines and improve productivity using Azure DatabricksKey Features: Get to grips with the distributed training and deployment of machine learning and deep learning modelsLearn how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep learning and machine learning models in a distributed computing infrastructureBook Description: Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines.The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks.Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.What You Will Learn: Create ETLs for big data in Azure DatabricksTrain, manage, and deploy machine learning and deep learning modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep learningFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environmentWho this book is for: This book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.
| Publisher | Packt Publishing |
| Publication date | May 25, 2021 |
| Language | English |
| Print length | 414 pages |
| ISBN-10 | 183864721X |
| ISBN-13 | 978-1838647216 |
| Item Weight | 708 g |
| Dimensions | 7.5 x 0.94 x 9.25 inches (19.1 x 2.4 x 23.5 cm) |
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English Edition Alan Bernardo Palacio Format: Paperback Editorial Review
Distributed Data Systems with Azure Databricks: Create deploy and manage enterprise data pipelines delivers a comprehensive guide for professionals seeking to harness Azure Databricks for building robust data pipelines. Published by Packt Publishing, this book spans 414 pages and offers valuable insights into establishing scalable data systems. Users have praised its clear explanations and practical examples, making complex topics more accessible. The publication focuses on best practices and real-world applications, ensuring that readers can effectively implement concepts in their own projects. With its well-structured content, this resource is ideal for data engineers and architects aiming to enhance their skills and expertise.
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Pros
- Comprehensive coverage of Azure Databricks
- Clear explanations and practical examples
- Focus on real-world applications
- Ideal for data engineers and architects
- Well-structured and easy to follow
Cons
- Might be too technical for beginners
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