Distributed Data Systems With Azure Databricks

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Distributed Data Systems With Azure Databricks

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Author : Alan Bernardo Palacio
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-05-25

Distributed Data Systems With Azure Databricks written by Alan Bernardo Palacio and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-25 with Computers categories.

Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key FeaturesGet 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 learnCreate 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.

Cloud Scale Analytics With Azure Data Services

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Author : Patrik Borosch
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-07-23

Cloud Scale Analytics With Azure Data Services written by Patrik Borosch and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-23 with Computers categories.

A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key FeaturesStore and analyze data with enterprise-grade security and auditingPerform batch, streaming, and interactive analytics to optimize your big data solutions with easeDevelop and run parallel data processing programs using real-world enterprise scenariosBook Description Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learnImplement data governance with Azure servicesUse integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure MonitorExplore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wranglingImplement networking with Synapse Analytics and Spark poolsCreate and run Spark jobs with Databricks clustersImplement streaming using Azure Functions, a serverless runtime environment on AzureExplore the predefined ML services in Azure and use them in your appWho this book is for This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required.

Optimizing Databricks Workloads

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Author : Anirudh Kala
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-12-24

Optimizing Databricks Workloads written by Anirudh Kala and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-24 with Computers categories.

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 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 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 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.

Distributed Machine Learning With Python

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Author : Guanhua Wang
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29

Distributed Machine Learning With Python written by Guanhua Wang and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.

Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloud Key FeaturesAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook Description Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner. What you will learnDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is for This book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.

Machine Learning Engineering With Mlflow

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Author : Natu Lauchande
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-08-27

Machine Learning Engineering With Mlflow written by Natu Lauchande and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-27 with Computers categories.

Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments. What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.

Azure Modern Data Architecture

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Author : Anouar BEN ZAHRA
language : en
Publisher: Anouar BEN ZAHRA
Release Date :

Azure Modern Data Architecture written by Anouar BEN ZAHRA and has been published by Anouar BEN ZAHRA this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Key Features Discover the key drivers of successful Azure architecture Practical guidance Focus on scalability and performance Expert authorship Book Description This book presents a guide to design and implement scalable, secure, and efficient data solutions in the Azure cloud environment. It provides Data Architects, developers, and IT professionals who are responsible for designing and implementing data solutions in the Azure cloud environment with the knowledge and tools needed to design and implement data solutions using the latest Azure data services. It covers a wide range of topics, including data storage, data processing, data analysis, and data integration. In this book, you will learn how to select the appropriate Azure data services, design a data processing pipeline, implement real-time data processing, and implement advanced analytics using Azure Databricks and Azure Synapse Analytics. You will also learn how to implement data security and compliance, including data encryption, access control, and auditing. Whether you are building a new data architecture from scratch or migrating an existing on premises solution to Azure, the Azure Data Architecture Guidelines are an essential resource for any organization looking to harness the power of data in the cloud. With these guidelines, you will gain a deep understanding of the principles and best practices of Azure data architecture and be equipped to build data solutions that are highly scalable, secure, and cost effective. What You Need to Use this Book? To use this book, it is recommended that readers have a basic understanding of data architecture concepts and data management principles. Some familiarity with cloud computing and Azure services is also helpful. The book is designed for data architects, data engineers, data analysts, and anyone involved in designing, implementing, and managing data solutions on the Azure cloud platform. It is also suitable for students and professionals who want to learn about Azure data architecture and its best practices.

Mc Microsoft Certified Azure Data Fundamentals Study Guide

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Author : Jake Switzer
language : en
Publisher: John Wiley & Sons
Release Date : 2022-04-14

Mc Microsoft Certified Azure Data Fundamentals Study Guide written by Jake Switzer and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-14 with Computers categories.

The most authoritative and complete study guide for people beginning to work with data in the Azure cloud In MC Azure Data Fundamentals Study Guide: Exam DP-900, expert Cloud Solution Architect Jake Switzer delivers a hands-on blueprint to acing the DP-900 Azure data certification. The book prepares you for the test – and for a new career in Azure data analytics, architecture, science, and more – with a laser-focus on the job roles and responsibilities of Azure data professionals. You’ll receive a foundational knowledge of core data concepts, like relational and non-relational data and transactional and analytical data workloads, while diving deep into every competency covered on the DP-900 exam. You’ll also get: Access to complimentary online study tools, including hundreds of practice exam questions, electronic flashcards, and a searchable glossary Additional prep assistance with access to Sybex’s superior interactive online learning environment and test bank Walkthroughs of skills and knowledge that are absolutely necessary for current and aspiring Azure data pros in introductory roles Perfect for anyone just beginning to work with data in the cloud, MC Azure Data Fundamentals Study Guide: Exam DP-900 is a can’t-miss resource for anyone prepping for the DP-900 exam or considering a new career working with Azure data.

Exam Ref Dp 900 Microsoft Azure Data Fundamentals

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Author : Nicola Farquharson
language : en
Publisher: Microsoft Press
Release Date : 2024-04-22

Exam Ref Dp 900 Microsoft Azure Data Fundamentals written by Nicola Farquharson and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-22 with Computers categories.

Prepare for Microsoft Exam DP-900 and demonstrate your real-world foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services. Designed for business users, functional consultants, and other professionals, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Fundamentals level. Focus on the expertise measured by these objectives: Describe core data concepts within Microsoft Azure Describe how to work with both relational and non-relational data types on Azure Describe strategic application, data management, storage and processing in the Azure cloud environment Describe an analytics workload on Azure This Microsoft Exam Ref: Organizes its coverage by the Skills Measured list published for the exam Features strategic, what-if scenarios to challenge you Assumes you have foundational knowledge of core data concepts and their implementation with Microsoft Azure data services, and a general grasp of cloud concepts

Designing Distributed Systems

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Author : Brendan Burns
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-02-20

Designing Distributed Systems written by Brendan Burns and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-20 with Computers categories.

Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows

Azure Data And Ai Architect Handbook

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Author : Olivier Mertens
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31

Azure Data And Ai Architect Handbook written by Olivier Mertens and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Computers categories.

Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions Purchase of the print or Kindle book includes a free PDF eBook Key Features Translate and implement conceptual architectures with the right Azure services Inject artificial intelligence into data solutions for advanced analytics Leverage cloud computing and frameworks to drive data science workloads Book DescriptionWith data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.What you will learn Design scalable and cost-effective cloud data platforms on Microsoft Azure Explore architectural design patterns with various use cases Determine the right data stores and data warehouse solutions Discover best practices for data orchestration and transformation Help end users to visualize data using interactive dashboarding Leverage OpenAI and custom ML models for advanced analytics Manage security, compliance, and governance for the data estate Who this book is forThis book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird’s-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended.