Download or read book Data Engineering with Apache Spark Delta Lake and Lakehouse written by Manoj Kukreja and published by Packt Publishing Ltd. This book was released on 2021-10-22 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Modern Data Architectures with Python written by Brian Lipp and published by Packt Publishing Ltd. This book was released on 2023-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Modern Data Architecture on AWS written by Behram Irani and published by Packt Publishing Ltd. This book was released on 2023-08-31 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Mastering the Modern Data Stack written by Nick Jewell, PhD and published by TinyTechMedia LLC. This book was released on 2023-09-28 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!™
Download or read book Building Modern Data Applications Using Databricks Lakehouse written by Will Girten and published by Packt Publishing Ltd. This book was released on 2024-10-21 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop, optimize, and monitor data pipelines on Databricks
Download or read book Data Engineering with Scala and Spark written by Eric Tome and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Mastering Azure Synapse Analytics guide to modern data integration written by Sultan Yerbulatov and published by Litres. This book was released on 2024-06-26 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Engineering with Databricks Cookbook written by Pulkit Chadha and published by Packt Publishing Ltd. This book was released on 2024-05-31 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Delta Lake The Definitive Guide written by Denny Lee and published by "O'Reilly Media, Inc.". This book was released on 2024-10-30 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Data Engineering for AI ML Pipelines written by Venkata Karthik Penikalapati and published by BPB Publications. This book was released on 2024-10-18 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION Data engineering is the art of building and managing data pipelines that enable efficient data flow for AI/ML projects. This book serves as a comprehensive guide to data engineering for AI/ML systems, equipping you with the knowledge and skills to create robust and scalable data infrastructure. This book covers everything from foundational concepts to advanced techniques. It begins by introducing the role of data engineering in AI/ML, followed by exploring the lifecycle of data, from data generation and collection to storage and management. Readers will learn how to design robust data pipelines, transform data, and deploy AI/ML models effectively for real-world applications. The book also explains security, privacy, and compliance, ensuring responsible data management. Finally, it explores future trends, including automation, real-time data processing, and advanced architectures, providing a forward-looking perspective on the evolution of data engineering. By the end of this book, you will have a deep understanding of the principles and practices of data engineering for AI/ML. You will be able to design and implement efficient data pipelines, select appropriate technologies, ensure data quality and security, and leverage data for building successful AI/ML models. KEY FEATURES ● Comprehensive guide to building scalable AI/ML data engineering pipelines. ● Practical insights into data collection, storage, processing, and analysis. ● Emphasis on data security, privacy, and emerging trends in AI/ML. WHAT YOU WILL LEARN ● Architect scalable data solutions for AI/ML-driven applications. ● Design and implement efficient data pipelines for machine learning. ● Ensure data security and privacy in AI/ML systems. ● Leverage emerging technologies in data engineering for AI/ML. ● Optimize data transformation processes for enhanced model performance. WHO THIS BOOK IS FOR This book is ideal for software engineers, ML practitioners, IT professionals, and students wanting to master data pipelines for AI/ML. It is also valuable for developers and system architects aiming to expand their knowledge of data-driven technologies. TABLE OF CONTENTS 1. Introduction to Data Engineering for AI/ML 2. Lifecycle of AI/ML Data Engineering 3. Architecting Data Solutions for AI/ML 4. Technology Selection in AI/ML Data Engineering 5. Data Generation and Collection for AI/ML 6. Data Storage and Management in AI/ML 7. Data Ingestion and Preparation for ML 8. Transforming and Processing Data for AI/ML 9. Model Deployment and Data Serving 10. Security and Privacy in AI/ML Data Engineering 11. Emerging Trends and Future Direction
Download or read book Data Engineering written by Phil Gilberts and published by Freegulls Publishing House. This book was released on with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the world of data engineering, where the raw material of the digital age—data—is transformed into actionable insights that drive decisions, innovations, and advancements across industries. This book is your gateway into understanding and mastering the essential principles, practices, and technologies that underpin the field of data engineering. In today's data-driven economy, organizations increasingly rely on robust data infrastructures and efficient data pipelines to harness the power of information. Data engineering is the backbone of this infrastructure, encompassing the design, implementation, and maintenance of systems that enable the collection, storage, and processing of vast amounts of data. This book is designed as a comprehensive guide for anyone seeking to embark on a journey into data engineering or looking to deepen their understanding of its intricacies. Whether you are a seasoned data professional, a software engineer transitioning into data roles, or a student eager to explore the forefront of technological innovation, this book will equip you with the knowledge and skills necessary to navigate the complexities of modern data ecosystems. Each chapter is crafted to provide a blend of theoretical foundations, practical insights, and hands-on examples to help you on your way. So, let’s get started!
Download or read book Data Engineering with AWS written by Gareth Eagar and published by Packt Publishing Ltd. This book was released on 2023-10-31 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Key Features Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Stay up to date with a comprehensive revised chapter on Data Governance Build modern data platforms with a new section covering transactional data lakes and data mesh Book DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learn Seamlessly ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Load data into a Redshift data warehouse and run queries with ease Visualize and explore data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Build transactional data lakes using Apache Iceberg with Amazon Athena Learn how a data mesh approach can be implemented on AWS Who this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
Download or read book Azure Modern Data Architecture written by Anouar BEN ZAHRA and published by Anouar BEN ZAHRA. This book was released on with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Mastering Apache Spark written by Cybellium Ltd and published by Cybellium Ltd. This book was released on 2023-09-26 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Potential of Distributed Data Processing with Apache Spark Are you prepared to venture into the realm of distributed data processing and analytics with Apache Spark? "Mastering Apache Spark" is your comprehensive guide to unlocking the full potential of this powerful framework for big data processing. Whether you're a data engineer seeking to optimize data pipelines or a business analyst aiming to extract insights from massive datasets, this book equips you with the knowledge and tools to master the art of Spark-based data processing. Key Features: 1. Deep Dive into Apache Spark: Immerse yourself in the core principles of Apache Spark, comprehending its architecture, components, and versatile functionalities. Construct a robust foundation that empowers you to manage big data with precision. 2. Installation and Configuration: Master the art of installing and configuring Apache Spark across diverse platforms. Learn about cluster setup, resource allocation, and configuration tuning for optimal performance. 3. Spark Core and RDDs: Uncover the core of Spark—Resilient Distributed Datasets (RDDs). Explore the functional programming paradigm and leverage RDDs for efficient and fault-tolerant data processing. 4. Structured Data Processing with Spark SQL: Delve into Spark SQL for querying structured data with ease. Learn how to execute SQL queries, perform data manipulations, and tap into the power of DataFrames. 5. Streamlining Data Processing with Spark Streaming: Discover the power of real-time data processing with Spark Streaming. Learn how to handle continuous data streams and perform near-real-time analytics. 6. Machine Learning with MLlib: Master Spark's machine learning library, MLlib. Dive into algorithms for classification, regression, clustering, and recommendation, enabling you to develop sophisticated data-driven models. 7. Graph Processing with GraphX: Embark on a journey through graph processing with Spark's GraphX. Learn how to analyze and visualize graph data to glean insights from complex relationships. 8. Data Processing with Spark Structured Streaming: Explore the world of structured streaming in Spark. Learn how to process and analyze data streams with the declarative power of DataFrames. 9. Spark Ecosystem and Integrations: Navigate Spark's rich ecosystem of libraries and integrations. From data ingestion with Apache Kafka to interactive analytics with Apache Zeppelin, explore tools that enhance Spark's capabilities. 10. Real-World Applications: Gain insights into real-world use cases of Apache Spark across industries. From fraud detection to sentiment analysis, discover how organizations leverage Spark for data-driven innovation. Who This Book Is For: "Mastering Apache Spark" is a must-have resource for data engineers, analysts, and IT professionals poised to excel in the world of distributed data processing using Spark. Whether you're new to Spark or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of this transformative framework.
Download or read book Fundamentals of Data Engineering written by Joe Reis and published by "O'Reilly Media, Inc.". This book was released on 2022-06-22 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle
Download or read book Stream Processing with Apache Spark written by Gerard Maas and published by "O'Reilly Media, Inc.". This book was released on 2019-06-05 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
