Data Engineering with Apache Spark, Delta Lake, and Lakehouse PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Data Engineering with Apache Spark, Delta Lake, and Lakehouse PDF full book. Access full book title Data Engineering with Apache Spark, Delta Lake, and Lakehouse by Manoj Kukreja. Download full books in PDF and EPUB format.
Author: Manoj Kukreja Publisher: Packt Publishing Ltd ISBN: 1801074321 Category : Computers Languages : en Pages : 480
Book Description
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Author: Manoj Kukreja Publisher: Packt Publishing Ltd ISBN: 1801074321 Category : Computers Languages : en Pages : 480
Book Description
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Author: Dr. RVS Praveen Publisher: Addition Publishing House ISBN: 9364225716 Category : Antiques & Collectibles Languages : en Pages : 220
Book Description
A resource designed for anybody interested in comprehending the whole lifecycle of data management in the current digital era is Data Engineering for Modern Applications. The book is organised into parts that systematically address key subjects. An introduction to data engineering principles is given first, followed by a thorough examination of data pipelines, storage options, and data transformation techniques. Data orchestration systems, cloud services, and distributed computing are just a few of the specialised tools and platforms that are being addressed in depth as the discipline of data engineering develops. This book places a lot of emphasis on using data engineering concepts in practical situations. The purpose of the chapters is to demonstrate best practices for creating, implementing, and overseeing scalable and effective data pipelines. Data Engineering for Modern Applications offers a useful framework that is easily applicable in a range of fields by including real-world examples and case studies. The book also discusses how data engineering supports AI and machine learning, outlining the procedures that guarantee data availability, consistency, and quality for these cutting-edge applications. This book serves as a manual for engineers, data scientists, and business professionals who are dedicated to using data in a future where decisions are made based on facts. This thorough guide will provide readers with the knowledge and self-assurance they need to address data difficulties, adjust to new technologies, and eventually help current data-driven systems be implemented successfully.
Author: Brian Lipp Publisher: Packt Publishing Ltd ISBN: 1801076413 Category : Computers Languages : en Pages : 318
Book Description
Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is forThis book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.
Author: Behram Irani Publisher: Packt Publishing Ltd ISBN: 1801810125 Category : Computers Languages : en Pages : 420
Book Description
Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.
Author: Nick Jewell, PhD Publisher: TinyTechMedia LLC ISBN: Category : Computers Languages : en Pages : 129
Book Description
In the age of digital transformation, becoming overwhelmed by the sheer volume of potential data management, analytics, and AI solutions is common. Then it's all too easy to become distracted by glossy vendor marketing, and then chase the latest shiny tool, rather than focusing on building resilient, valuable platforms that will outperform the competition. This book aims to fix a glaring gap for data professionals: a comprehensive guide to the full Modern Data Stack that's rooted in real-world capabilities, not vendor hype. It is full of hard-earned advice on how to get maximum value from your investments through tangible insights, actionable strategies, and proven best practices. It comprehensively explains how the Modern Data Stack is truly utilized by today's data-driven companies. Mastering the Modern Data Stack: An Executive Guide to Unified Business Analytics is crafted for a diverse audience. It's for business and technology leaders who understand the importance and potential value of data, analytics, and AI—but don’t quite see how it all fits together in the big picture. It's for enterprise architects and technology professionals looking for a primer on the data analytics domain, including definitions of essential components and their usage patterns. It's also for individuals early in their data analytics careers who wish to have a practical and jargon-free understanding of how all the gears and pulleys move behind the scenes in a Modern Data Stack to turn data into actual business value. Whether you're starting your data journey with modest resources, or implementing digital transformation in the cloud, you'll find that this isn't just another textbook on data tools or a mere overview of outdated systems. It's a powerful guide to efficient, modern data management and analytics, with a firm focus on emerging technologies such as data science, machine learning, and AI. If you want to gain a competitive advantage in today’s fast-paced digital world, this TinyTechGuide™ is for you. Remember, it’s not the tech that’s tiny, just the book!™
Author: Sultan Yerbulatov Publisher: Litres ISBN: 5046527766 Category : Computers Languages : en Pages : 233
Book Description
Drawing from my extensive hands-on experience as a data engineer, this book presents a deep exploration of Azure Synapse Analytics through detailed explanations, practical examples, and expert insights. Readers will learn to navigate the complexities of modern data analytics, from data ingestion and transformation to dynamic data masking and compliance reporting.
Author: Eric Tome Publisher: Packt Publishing Ltd ISBN: 1804614327 Category : Computers Languages : en Pages : 300
Book Description
Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD) Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Get to grips with polymorphic functions, type parameterization, and Scala implicits Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Profile and clean your data using Deequ Performance tune your data pipelines using Scala Who this book is for This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.
Author: Pulkit Chadha Publisher: Packt Publishing Ltd ISBN: 1837632065 Category : Computers Languages : en Pages : 438
Book Description
Work through 70 recipes for implementing reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake, and use Databricks to orchestrate and govern your data Key Features Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake Gain practical guidance on using Delta Lake tables and orchestrating data pipelines Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.What you will learn Perform data loading, ingestion, and processing with Apache Spark Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark Manage and optimize Delta tables with Apache Spark and Delta Lake APIs Use Spark Structured Streaming for real-time data processing Optimize Apache Spark application and Delta table query performance Implement DataOps and DevOps practices on Databricks Orchestrate data pipelines with Delta Live Tables and Databricks Workflows Implement data governance policies with Unity Catalog Who this book is for This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming.
Author: Denny Lee Publisher: "O'Reilly Media, Inc." ISBN: 1098151909 Category : Computers Languages : en Pages : 391
Book Description
Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques. Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale. This book helps you: Understand key data reliability challenges and how Delta Lake solves them Explain the critical role of Delta transaction logs as a single source of truth Learn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and Trino Architect data lakehouses with the medallion architecture Optimize Delta Lake performance with features like deletion vectors and liquid clustering
Author: Manoj Kukreja
Author: Manoj Kukreja
Author: Dr. RVS Praveen
Author: Brian Lipp
Author: Behram Irani
Author: Nick Jewell, PhD
Author: Will Girten
Author: Sultan Yerbulatov
Author: Eric Tome
Author: Pulkit Chadha
Author: Denny Lee