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| data lake technology stack: Practical Data Science Andreas François Vermeulen, 2018-02-22 Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers |
| data lake technology stack: Practical Data Science Andreas François Vermeulen, 2018-02-21 Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers |
| data lake technology stack: Data Lake for Enterprises Tomcy John, Pankaj Misra, 2017-05-31 A practical guide to implementing your enterprise data lake using Lambda Architecture as the base About This Book Build a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the base Delve into the big data technologies required to meet modern day business strategies A highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-cases Who This Book Is For Java developers and architects who would like to implement a data lake for their enterprise will find this book useful. If you want to get hands-on experience with the Lambda Architecture and big data technologies by implementing a practical solution using these technologies, this book will also help you. What You Will Learn Build an enterprise-level data lake using the relevant big data technologies Understand the core of the Lambda architecture and how to apply it in an enterprise Learn the technical details around Sqoop and its functionalities Integrate Kafka with Hadoop components to acquire enterprise data Use flume with streaming technologies for stream-based processing Understand stream- based processing with reference to Apache Spark Streaming Incorporate Hadoop components and know the advantages they provide for enterprise data lakes Build fast, streaming, and high-performance applications using ElasticSearch Make your data ingestion process consistent across various data formats with configurability Process your data to derive intelligence using machine learning algorithms In Detail The term Data Lake has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake. Style and approach The book takes a pragmatic approach, showing ways to leverage big data technologies and lambda architecture to build an enterprise-level data lake. |
| data lake technology stack: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| data lake technology stack: Practical Enterprise Data Lake Insights Saurabh Gupta, Venkata Giri, 2018-07-29 Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the nuances of handling non-relational data. Starting from sourcing data into the Hadoop ecosystem, you will go through stages that can bring up tough questions such as data processing, data querying, and security. Concepts such as change data capture and data streaming are covered. The book takes an end-to-end solution approach in a data lake environment that includes data security, high availability, data processing, data streaming, and more. Each chapter includes application of a concept, code snippets, and use case demonstrations to provide you with a practical approach. You will learn the concept, scope, application, and starting point. What You'll Learn Get to know data lake architecture and design principles Implement data capture and streaming strategies Implement data processing strategies in Hadoop Understand the data lake security framework and availability model Who This Book Is For Big data architects and solution architects |
| data lake technology stack: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| data lake technology stack: Mastering the Modern Data Stack Nick Jewell, PhD, 2023-09-28 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!™ |
| data lake technology stack: Azure Data Factory by Example Richard Swinbank, |
| data lake technology stack: AWS Certified Data Analytics Study Guide with Online Labs Asif Abbasi, 2021-04-13 Virtual, hands-on learning labs allow you to apply your technical skills in realistic environments. So Sybex has bundled AWS labs from XtremeLabs with our popular AWS Certified Data Analytics Study Guide to give you the same experience working in these labs as you prepare for the Certified Data Analytics Exam that you would face in a real-life application. These labs in addition to the book are a proven way to prepare for the certification and for work as an AWS Data Analyst. AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is intended for individuals who perform in a data analytics-focused role. This UPDATED exam validates an examinee's comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data. It assesses an examinee's ability to define AWS data analytics services and understand how they integrate with each other; and explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization. The book focuses on the following domains: • Collection • Storage and Data Management • Processing • Analysis and Visualization • Data Security This is your opportunity to take the next step in your career by expanding and validating your skills on the AWS cloud. AWS is the frontrunner in cloud computing products and services, and the AWS Certified Data Analytics Study Guide: Specialty exam will get you fully prepared through expert content, and real-world knowledge, key exam essentials, chapter review questions, and much more. Written by an AWS subject-matter expert, this study guide covers exam concepts, and provides key review on exam topics. Readers will also have access to Sybex's superior online interactive learning environment and test bank, including chapter tests, practice exams, a glossary of key terms, and electronic flashcards. And included with this version of the book, XtremeLabs virtual labs that run from your browser. The registration code is included with the book and gives you 6 months of unlimited access to XtremeLabs AWS Certified Data Analytics Labs with 3 unique lab modules based on the book. |
| data lake technology stack: Medical Affairs Kirk V. Shepard, Charlotte Kremer, Garth Sundem, 2024-01-30 Medical Affairs is one of the three strategic pillars of the pharmaceutical and MedTech industries, but while clear career paths exist for Commercial and Research and Development, there is no formal training structure for Medical Affairs professionals. Medical and scientific expertise is a prerequisite for entry into the function, and many people transitioning into Medical Affairs have advanced degrees such as PhD, MD, or PharmD. However, these clinical/scientific experts may not be especially well-versed in aspects of industry such as the drug development lifecycle, crossfunctional collaborations within industry, and digital tools that are transforming the ways Medical Affairs generates and disseminates knowledge. This primer for aspiring and early-career Medical Affairs professionals equips readers with the baseline skills and understanding to excel across roles. Features: Defines the purpose and value of Medical Affairs and provides clear career paths for scientific experts seeking their place within the pharmaceutical and MedTech industries. Provides guidance and baseline competencies for roles within Medical Affairs including Medical Communications, Evidence Generation, Field Medical, Compliance, and many others. Specifies the true north of the Medical Affairs profession as ensuring patients receive maximum benefit from industry innovations including drugs, diagnostics and devices. Presents the purpose and specific roles of Medical Affairs roles across organization types including biotechs, small/medium/large pharma and device/diagnostic companies, taking into account adjustments in the practice of Medical Affairs to meet the needs of developing fields such as rare disease and gene therapy. Leverages the expertise of over 60 Medical Affairs leaders across companies, representing the first unified, global understanding of the Medical Affairs profession. |
| data lake technology stack: Cracking the Data Engineering Interview Kedeisha Bryan, Taamir Ransome, 2023-11-07 Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employers Key Features Develop your own brand, projects, and portfolio with expert help to stand out in the interview round Get a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modeling Practice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical rounds Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPreparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey. The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you’ll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you’ll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions. By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.What you will learn Create maintainable and scalable code for unit testing Understand the fundamental concepts of core data engineering tasks Prepare with over 100 behavioral and technical interview questions Discover data engineer archetypes and how they can help you prepare for the interview Apply the essential concepts of Python and SQL in data engineering Build your personal brand to noticeably stand out as a candidate Who this book is for If you’re an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite. |
| data lake technology stack: The Informed Company Dave Fowler, Matthew C. David, 2021-10-26 Learn how to manage a modern data stack and get the most out of data in your organization! Thanks to the emergence of new technologies and the explosion of data in recent years, we need new practices for managing and getting value out of data. In the modern, data driven competitive landscape the best guess approach—reading blog posts here and there and patching together data practices without any real visibility—is no longer going to hack it. The Informed Company provides definitive direction on how best to leverage the modern data stack, including cloud computing, columnar storage, cloud ETL tools, and cloud BI tools. You'll learn how to work with Agile methods and set up processes that's right for your company to use your data as a key weapon for your success . . . You'll discover best practices for every stage, from querying production databases at a small startup all the way to setting up data marts for different business lines of an enterprise. In their work at Chartio, authors Fowler and David have learned that most businesspeople are almost completely self-taught when it comes to data. If they are using resources, those resources are outdated, so they're missing out on the latest cloud technologies and advances in data analytics. This book will firm up your understanding of data and bring you into the present with knowledge around what works and what doesn't. Discover the data stack strategies that are working for today's successful small, medium, and enterprise companies Learn the different Agile stages of data organization, and the right one for your team Learn how to maintain Data Lakes and Data Warehouses for effective, accessible data storage Gain the knowledge you need to architect Data Warehouses and Data Marts Understand your business's level of data sophistication and the steps you can take to get to level up your data The Informed Company is the definitive data book for anyone who wants to work faster and more nimbly, armed with actionable decision-making data. |
| data lake technology stack: Data Mesh in Action Jacek Majchrzak, Sven Balnojan, Marian Siwiak, 2023-03-21 Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. In Data Mesh in Action you will learn how to: Implement a data mesh in your organization Turn data into a data product Move from your current data architecture to a data mesh Identify data domains, and decompose an organization into smaller, manageable domains Set up the central governance and local governance levels over data Balance responsibilities between the two levels of governance Establish a platform that allows efficient connection of distributed data products and automated governance Data Mesh in Action reveals how this groundbreaking architecture looks for both small startups and large enterprises. You won’t need any new technology—this book shows you how to start implementing a data mesh with flexible processes and organizational change. You’ll explore both an extended case study and multiple real-world examples. As you go, you’ll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition. About the technology Business increasingly relies on efficiently storing and accessing large volumes of data. The data mesh is a new way to decentralize data management that radically improves security and discoverability. A well-designed data mesh simplifies self-service data consumption and reduces the bottlenecks created by monolithic data architectures. About the book Data Mesh in Action teaches you pragmatic ways to decentralize your data and organize it into an effective data mesh. You’ll start by building a minimum viable data product, which you’ll expand into a self-service data platform, chapter-by-chapter. You’ll love the book’s unique “sliders” that adjust the mesh to meet your specific needs. You’ll also learn processes and leadership techniques that will change the way you and your colleagues think about data. What's inside Decompose an organization into manageable domains Turn data into a data product Set up central and local governance levels Build a fit-for-purpose data platform Improve management, initiation, and support techniques About the reader For data professionals. Requires no specific programming stack or data platform. About the author Jacek Majchrzak is a hands-on lead data architect. Dr. Sven Balnojan manages data products and teams. Dr. Marian Siwiak is a data scientist and a management consultant for IT, scientific, and technical projects. Table of Contents PART 1 FOUNDATIONS 1 The what and why of the data mesh 2 Is a data mesh right for you? 3 Kickstart your data mesh MVP in a month PART 2 THE FOUR PRINCIPLES IN PRACTICE 4 Domain ownership 5 Data as a product 6 Federated computational governance 7 The self-serve data platform PART 3 INFRASTRUCTURE AND TECHNICAL ARCHITECTURE 8 Comparing self-serve data platforms 9 Solution architecture design |
| data lake technology stack: The Rise of the Platform Marketer Craig Dempster, John Lee, 2015-04-09 Develop the skills and capabilities quickly becoming essential in the new marketing paradigm The Rise of the Platform Marketer helps you leverage the always-on consumer to deliver more personalized engagements across media, channels, and devices. By managing these interactions at scale throughout the customer lifecycle, you can optimize the value of your customers and segments through strategic use of Connected CRM (cCRM). This book shows you how to take advantage of the massive growth and proliferation of social and other digital media, with clear strategy for developing the new capabilities, tools, metrics, and processes essential in the age of platform marketing. Coverage includes identity management, audience management, consumer privacy and compliance, media and channel optimization, measurement and attribution, experience design, and integrated technology, plus a discussion on how the company as a whole must evolve to keep pace with marketing's increasingly rapid evolution and capabilities. The expansion of digital platforms has created addressability opportunity through search, video, display, and social media, offering today's foremost opportunity for competitive advantage. This book outlines the capabilities and perspective required to reap the rewards, helping you shift your strategy to align with the demands and expectations of the modern consumer. Develop the tools, metrics, and processes necessary to engage the modern consumer Gain a deep understanding of Connected Customer Relationship Management Leverage trends in technology and analytics to create targeted messages Adjust your company's structure and operations to align with new capabilities The new era of marketing requires thorough understanding of cCRM, along with the knowledge and innovative forethought to thrive in the ever-expanding digital audience platform environment. The Rise of the Platform Marketer gives you an edge, and helps you clear a path to full implementation. |
| data lake technology stack: Data Quality Fundamentals Barr Moses, Lior Gavish, Molly Vorwerck, 2022-09 Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the good pipelines, bad data problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets |
| data lake technology stack: Mastering Azure Synapse Analytics , 2023-04-15 A practical guide that will help you transform your data into actionable insights with Azure Synapse Analytics KEY FEATURES ● Explore the different features in the Azure Synapse Analytics workspace. ● Learn how to integrate Power BI and Data Governance capabilities with Azure Synapse Analytics. ● Accelerate your analytics journey with the no-code/low-code capabilities of Azure Synapse. DESCRIPTION Cloud analytics is a crucial aspect of any digital transformation initiative, and the capabilities of the Azure Synapse analytics platform can simplify and streamline this process. By mastering Azure Synapse Analytics, analytics developers across organizations can boost their productivity by utilizing low-code, no-code, and traditional code-based analytics frameworks. This book starts with a comprehensive introduction to Azure Synapse Analytics and its limitless cloud-scale analytics capabilities. You will then learn how to explore and work with data warehousing features in Azure Synapse. Moving on, the book will guide you on how to effectively use Synapse Spark for data engineering and data science. It will help you learn how to gain insights from your data through Observational analytics using Synapse Data Explorer. You will also discover the seamless data integration capabilities of Synapse Pipeline, and delve into the benefits of Synapse Analytics' low-code and no-code pipeline development features. Lastly the book will show you how to create network topology and implement industry-specific architecture patterns in Azure Synapse Analytics. By the end of the book, you will be able to process and analyze vast amounts of data in real-time to gain insights quickly and make informed decisions. WHAT YOU WILL LEARN ● Leverage Synapse Spark for machine learning tasks. ● Use Synapse Data Explorer for telemetry analysis. ● Take advantage of Synapse's common data model-based database templates. ● Query data using T-SQL, KQL, and Spark SQL within Synapse. ● Integrate Microsoft Purview with Synapse for enhanced data governance. WHO THIS BOOK IS FOR This book is designed for Cloud data engineers with prior experience in Azure cloud computing, as well as Chief Data Officers (CDOs) and Data professionals, who want to use this unified platform for data ingestion, data warehousing, and big data analytics. TABLE OF CONTENTS 1. Cloud Analytics Concept 2. Introduction to Azure Synapse Analytics 3. Modern Data Warehouse with the Synapse SQL Pool 4. Query as a Service- Synapse Serverless SQL 5. Synapse Spark Pool Capability 6. Synapse Spark and Data Science 7. Learning Synapse Data Explorer 8. Synapse Data Integration 9. Synapse Link for HTAP 10. Azure Synapse -Unified Analytics Service 11. Synapse Workspace Ecosystem Integration 12. Azure Synapse Network Topology 13. Industry Cloud Analytics |
| data lake technology stack: Big Data Analytics: Applications, Hadoop Technologies and Hive Dr.P.Pushpa, Dr.V.Thamilarasi, Dr. S. Lakshmi Prabha, Mrs.Sudha Nagarajan, 2024-04-22 Dr.P.Pushpa, Lecturer, School of Software Engineering, East China University of Technology, Nanchang, Jiangxi, China. Dr.V.Thamilarasi, Assistant Professor, Department of Computer Science, Sri Sarada College for Women(Autonomous), Salem, Tamil Nadu, India. Dr. S. Lakshmi Prabha, Associate Professor, Department of Computer Science, Seethalakshmi Ramaswami College, Tiruchirappalli, Tamil Nadu, India. Mrs.Sudha Nagarajan, Assistant Professor, Department of Computer Science, Excel College for Commerce and Science, Komarapalayam, Namakkal, Tamil Nadu, India. |
| data lake technology stack: Mastering Salesforce Experience Cloud Lillie Beiting, Rachel Rogers, 2024-10-04 Your guide to unlocking business potential and technical mastery with essential to advanced strategies for launching and maintaining top-tier Experience Cloud sites effortlessly Key Features Empower your team and your organization to lead and maintain an Experience Cloud transformation Master out-of-the-box Experience Cloud features, custom development options, and development best practices Curate a consumer-friendly Experience Cloud site that maximizes value for your company, while keeping maintenance costs low Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEmpowering your target audience to interact effortlessly with you and your product offerings is a critical aspect of business in the modern era. Users expect easy, professional digital experiences when engaging with organizations. However, creating engagement applications from scratch is challenging, and connecting user behavior with your organization’s data is even more complex. Enter Salesforce Experience Cloud sites, website portals built on the Salesforce data model that seamlessly connects your user data to your user experience. Mastering Salesforce Experience Cloud focuses on the human-centric nature of this product, beginning with a comprehensive guide on designing for your organization’s desired users and ensuring success for both internal teams and end users. After exploring the real-world applications of Experience Cloud and reviewing license models, this book provides a beginning-to-end guide to mastering the technical backend of this product, covering both out-of-the-box settings and customization techniques. By the end of this book, you’ll have gained a deep understanding of the Experience Cloud data model and customization options to create engaging, user-centric digital experiences that deliver value to your organization and stakeholders. What you will learn Define your audience and identify your overall strategy for an Experience Cloud site Understand the technical and operational strategy needed to support your site Work with the Experience Cloud data model and standard template features Determine when to use Visualforce, Aura, LWC, or LWR while exploring custom development options Get to grips with the how Salesforce Flow and Triggers work Leverage marketing automation, knowledge base, and communication in the site Find out about site launch tactics, user creation, site moderation, and ongoing reporting Who this book is for If you want to understand the intricacies of Salesforce Experience Cloud, transform your client experience, enhance your enterprise architecture, and create a scalable, world class-customer web experience that smoothly integrates with an existing Salesforce instance, this book is for you. Business leaders, IT leaders, Salesforce developers, Salesforce admins, and web teams tasked with delivering and maintaining an excellent, integrated Experience Cloud portal will benefit from this book. Ideal for readers with Salesforce experience in any cloud or a basic grasp of Service Cloud features. |
| data lake technology stack: Data Engineering with AWS Gareth Eagar, 2023-10-31 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. |
| data lake technology stack: Wild West to Agile Jim Highsmith, 2023-05-17 Wild West to Agile: The evolution and revolution of software development, drawn from personal experience, from the Apollo moon mission to digital transformations. In 2023, technology is your business--no matter what your business. But how did we get here and how could a historical perspective prepare us for the future? Jim Highsmith tackles the evolution and revolution of software development, embellishes them with personal experiences, from the Apollo moon mission to modern digital transformations, and introduces the adventurous pioneers--from structured era developer Ken Orr to Agile methodologist Kent Beck--who strived to make the world a better place, by building better software. Jim's six-decade career has encompassed the Wild West (1966-1979), to Structured Methods and Monumental Methodologies (1980s), to the Roots of Agile (1990s), to the present Agile Era (2001-present). In each era, he explores the evolution of software development methods, methodologies, and mindsets. Whether you are from the 1970-1980's generation looking for an I was there too moment, a newer generation interested in the evolution of software development, the Agile generation interested in how Agile methodologies were born and evolved, or have a general interest in information technology, Wild West to Agile has something for you. Jim Highsmith is the Forrest Gump of software development. What made the 1994 movie so entertaining was how frequently Forrest found himself in the right spot as history was being made. Unlike Forrest, though, Jim's actions influenced that history. --Mike Cohn, cofounder of the Agile Alliance, and the Scrum Alliance; author of Succeeding with Agile If you want to understand the shape of software development today, this is the book for you. If you want to understand how to navigate a turbulent career with grace & style, this is also the book for you. If you enjoy memoirs, ditto. Enjoy his story. --Kent Beck, Chief Scientist, Mechanical Orchard; author, Extreme Programming Explained This entire journey--beginning with the Wild West era of software development through the Agile Era to today's Digital Transformation era--is entirely empowered by people. Thank you, Jim, for sharing these beautiful stories and honoring the people that were a part of this amazing journey. --Heidi J. Musser, Vice President and CIO, USAA, retired I've always felt that understanding history is important, because it's hard to understand where we are unless you understand the path that we took to get here. Jim's memoir is an entertaining and astute odyssey through this history. --Martin Fowler, Chief Scientist, Thoughtworks |
| data lake technology stack: Open-Source Security Operations Center (SOC) Alfred Basta, Nadine Basta, Waqar Anwar, Mohammad Ilyas Essar, 2024-11-20 A comprehensive and up-to-date exploration of implementing and managing a security operations center in an open-source environment In Open-Source Security Operations Center (SOC): A Complete Guide to Establishing, Managing, and Maintaining a Modern SOC, a team of veteran cybersecurity practitioners delivers a practical and hands-on discussion of how to set up and operate a security operations center (SOC) in a way that integrates and optimizes existing security procedures. You’ll explore how to implement and manage every relevant aspect of cybersecurity, from foundational infrastructure to consumer access points. In the book, the authors explain why industry standards have become necessary and how they have evolved – and will evolve – to support the growing cybersecurity demands in this space. Readers will also find: A modular design that facilitates use in a variety of classrooms and instructional settings Detailed discussions of SOC tools used for threat prevention and detection, including vulnerability assessment, behavioral monitoring, and asset discovery Hands-on exercises, case studies, and end-of-chapter questions to enable learning and retention Perfect for cybersecurity practitioners and software engineers working in the industry, Open-Source Security Operations Center (SOC) will also prove invaluable to managers, executives, and directors who seek a better technical understanding of how to secure their networks and products. |
| data lake technology stack: Blockchain, Fintech, and Islamic Finance Hazik Mohamed, Hassnian Ali, 2022-09-05 Following the success of the first edition that brought attention to the digital revolution in Islamic financial services, comes this revised and updated second edition of Blockchain, Fintech and Islamic Finance. The authors reiterate the potential of digital disruption to shrink the role and relevance of today’s banks, while simultaneously creating better, faster, cheaper services that will be an essential part of everyday life. Digital transformation will also offer the ability to create new ways to better comply to Islamic values in order to rebuild trust and confidence in the current financial system. In this new edition, they explore current concepts of decentralized finance (DeFi), distributed intelligence, stablecoins, and the integration of AI, blockchain, data analytics and IoT devices for a holistic solution to ensure technology adoption in a prudent and sustainable manner. The book discusses crucial innovation, structural and institutional developments for financial technologies including two fast-growing trends that merge and complement each other: tokenization, where all illiquid assets in the world, from private equity to real estate and luxury goods, become liquid and can be traded more efficiently, and second, the rise of a new tokenized economy where inevitably new rules and ways to enforce them will develop to fully unleash their capabilities. These complementary and oft-correlated trends will complete the decentralization of finance and will influence the way future financial services will be implemented. This book provides insights into the shift in processes, as well as the challenges that need to be overcome for practical applications for AI and blockchain and how to approach such innovations. It also covers new technological risks that are the consequence of utilizing frontier technologies such as AI, blockchain and IoT. Industry leaders, Islamic finance professionals, along with students and academics in the fields of Islamic finance and economics will benefit immensely from this book. |
| data lake technology stack: Practical Lakehouse Architecture Gaurav Ashok Thalpati, 2024-07-24 This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse |
| data lake technology stack: Creating Value with Data Analytics in Marketing Peter C. Verhoef, Edwin Kooge, Natasha Walk, Jaap E. Wieringa, 2021-11-07 This book is a refreshingly practical yet theoretically sound roadmap to leveraging data analytics and data science. The vast amount of data generated about us and our world is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organizations to leverage the information to create value in marketing. Creating Value with Data Analytics in Marketing provides a nuanced view of big data developments and data science, arguing that big data is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. The second edition of this bestselling text has been fully updated in line with developments in the field and includes a selection of new, international cases and examples, exercises, techniques and methodologies. Tying data and analytics to specific goals and processes for implementation makes this essential reading for advanced undergraduate and postgraduate students and specialists of data analytics, marketing research, marketing management and customer relationship management. Online resources include chapter-by-chapter lecture slides and data sets and corresponding R code for selected chapters. |
| data lake technology stack: A Bird's Eye view of Data Visualisation Nisarg Patel, Hetarth Shah, 2021-07-18 A bird's eye view of anything is usually the first step to enter a new field of interest. The vast field of Data Visualisation might seem overwhelming at first, but a bird's eye view with this book might just be enough to nudge you over that cliff. We will try to go through every little detail about Data Visualisation, from its etymology all the way to its future enhancements. A detailed analysis of various Data Visualisation and representation techniques will be done in this book along with its various applications as well as the challenges, in enough detail to make it easier to grasp for new explorers of this field. |
| data lake technology stack: Big Data Strategies for Agile Business Bhuvan Unhelkar, 2017-09-13 Agile is a set of values, principles, techniques, and frameworks for the adaptable, incremental, and efficient delivery of work. Big Data is a rapidly growing field that encompasses crucial aspects of data such as its volume, velocity, variety, and veracity. This book outlines a strategic approach to Big Data that will render a business Agile. It discusses the important competencies required to streamline and focus on the analytics and presents a roadmap for implementing such analytics in business. |
| data lake technology stack: Fintech Pranay Gupta, T. Mandy Tham, 2018-12-03 This extraordinary book, written by leading players in a burgeoning technology revolution, is about the merger of finance and technology (fintech), and covers its various aspects and how they impact each discipline within the financial services industry. It is an honest and direct analysis of where each segment of financial services will stand. Fintech: The New DNA of Financial Services provides an in-depth introduction to understanding the various areas of fintech and terminology such as AI, big data, robo-advisory, blockchain, cryptocurrency, InsurTech, cloud computing, crowdfunding and many more. Contributions from fintech innovators discuss banking, insurance and investment management applications, as well as the legal and human resource implications of fintech in the future. |
| data lake technology stack: Data Lakes Anne Laurent, Dominique Laurent, Cédrine Madera, 2020-06-03 The concept of a data lake is less than 10 years old, but they are already hugely implemented within large companies. Their goal is to efficiently deal with ever-growing volumes of heterogeneous data, while also facing various sophisticated user needs. However, defining and building a data lake is still a challenge, as no consensus has been reached so far. Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of a data lake; the management of metadata supplying key information about the stored data, master data and reference data; the roles of linked data and fog computing in a data lake ecosystem; and how gravity principles apply in the context of data lakes. A variety of case studies are also presented, thus providing the reader with practical examples of data lake management. |
| data lake technology stack: Social Big Data Analytics Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu, Kit Yan Chan, Amit Rudra, 2021-03-10 This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display. |
| data lake technology stack: Electronic Components and Systems for Automotive Applications Jochen Langheim, 2019-05-25 This volume collects selected papers of the 5th CESA Automotive Electronics Congress, Paris, 2018. CESA is the most important automotive electronics conference in France. The topical focus lies on state-of-the-art automotive electronics with respect to energy consumption and autonomous driving. The target audience primarily comprises industry leaders and research experts in the automotive industry. |
| data lake technology stack: Advances in Computational Intelligence and Communication Manolo Dulva Hina, Amar Ramdane-Cherif, Rafik Zitouni, Assia Soukane, 2022-12-13 This book presents select papers from the 2nd EAI International Conference on Computational Intelligence and Communications (CICom 2021). The papers reveal recent advances in the broader domains of Computational Intelligence including (1) automation, control, and intelligent transportation system; (2) big data, Internet of Things, and smart cities; (3) wireless communication systems and cyber security and; (4) human/brain-computer interfaces, and image and pattern recognition. The book demonstrates complex real-world problems in which mathematical or traditional modelling are not the preferred solution and posits alternative solutions. This collection of applications demonstrates the important advances in computational intelligence. The chapters present various ideas that will benefit researchers, graduate students and engineers in this domain. |
| data lake technology stack: Architecting Data and Machine Learning Platforms Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner, 2023-10-12 All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach |
| data lake technology stack: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management. |
| data lake technology stack: Hadoop Blueprints Anurag Shrivastava, Tanmay Deshpande, 2016-09-30 Use Hadoop to solve business problems by learning from a rich set of real-life case studies About This Book Solve real-world business problems using Hadoop and other Big Data technologies Build efficient data lakes in Hadoop, and develop systems for various business cases like improving marketing campaigns, fraud detection, and more Power packed with six case studies to get you going with Hadoop for Business Intelligence Who This Book Is For If you are interested in building efficient business solutions using Hadoop, this is the book for you This book assumes that you have basic knowledge of Hadoop, Java, and any scripting language. What You Will Learn Learn about the evolution of Hadoop as the big data platform Understand the basics of Hadoop architecture Build a 360 degree view of your customer using Sqoop and Hive Build and run classification models on Hadoop using BigML Use Spark and Hadoop to build a fraud detection system Develop a churn detection system using Java and MapReduce Build an IoT-based data collection and visualization system Get to grips with building a Hadoop-based Data Lake for large enterprises Learn about the coexistence of NoSQL and In-Memory databases in the Hadoop ecosystem In Detail If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space. Style and approach This is an example-driven book where each chapter covers a single business problem and describes its solution by explaining the structure of a dataset and tools required to process it. Every project is demonstrated with a step-by-step approach, and explained in a very easy-to-understand manner. |
| data lake technology stack: Data Lakehouse in Action Pradeep Menon, 2022-03-17 Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required. |
| data lake technology stack: Big Data Applications for Improving Library Services Dhamdhere, Sangeeta Namdev, 2020-09-25 Today, libraries must provide various web-based services, social media, and internet to patrons in order to adequately support their information needs. In addition to these services, the maintenance of online literature, databases, data sets, and archives cause librarians to have to handle huge amounts of data each day. Big data can support with quality improvement and problem solving to improve library services and can help librarians to provide up-to-date and innovative real-time services to library users. Big Data Applications for Improving Library Services is an essential scholarly publication that examines the implications and applications of big data analytics on services provided by libraries. Highlighting a wide range of topics such as data analytics, mobile technologies, and web-based services, this book is ideal for librarians, knowledge managers, data scientists, data analysts, cataloguers, academicians, IT professionals, researchers, and students. |
| data lake technology stack: Integrated Citizen Centered Digital Health and Social Care A. Värri, J. Delgado, P. Gallos, 2020-12-15 As citizens, we must all take responsibility for our own health to some extent, and recent developments in medical informatics have provided some valuable new ways to help us do that. This book presents the proceedings of the 2020 Special Topic Conference of the European Federation for Medical Informatics (EFMI STC 2020), held for the first time as a virtual conference on 26 & 27 November 2020, due to restrictions associated with the COVID-19 pandemic. Entitled Integrated citizen centered digital health and social care – Citizens as data producers and service co-creators, this conference focused on the citizen-centered aspects of health informatics. This topic provided the opportunity for contributors to present innovative solutions to allow citizens to take greater responsibility for their health with the help of information and communication technology, and the 52 presented papers published here cover a wide range of areas under the broad, invited subject headings of: tools and technologies to support citizen-centered digital services; capacity building to enhance the development and use of digital services; confidentiality, data integrity and data protection to guarantee trustworthy services; citizen safety in digital services; effectiveness and impact of citizen-digital and integrated health and social services; evaluation approaches and methods for digital services; usability, usefulness and user acceptance of digital services; and guidelines for the successful implementation of digital services for citizens. Offering a current overview of research and applications, the book will be of interest to all those health professionals working to increase citizen use of digital healthcare. |
| data lake technology stack: Mastering Data Engineering and Analytics with Databricks Manoj Kumar, 2024-09-30 TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index |
| data lake technology stack: The A.I. Marketer Andrew W. Pearson, 2019-04-15 We seem to be living in the age of A.I. Everywhere you look, companies are touting their most recent A.I., machine learning, and deep learning breakthroughs, even when they are far short of anything that could be touted as a “breakthrough.” “A.I.” has eclipsed “Blockchain” and “Crypto” as the buzzword of today. Indeed, one of the best ways to raise VC funding is to stick ‘AI’ or ‘ML’ at the front of your prospectus and “.ai” at the end of your website. Separating fact from fiction is more important than it has ever been. The A.I. Marketer breaks down A.I., machine learning, and deep learning into five unique use cases—sound, time series, text, image, and video—and also reveals how marketing executives can utilize this powerful technology to help them more finely tune their marketing campaigns, better segment their customers, increase lead generation, and foster strong customer loyalty. Today, “Personalization”—the process of utilizing mobile, social, geo-location data, web morphing, context and even affective computing to tailor messages and experiences to an individual interacting with them—is becoming the optimum word in a radically new customer intelligence environment. The A.I. Marketer explains this complex technology in simple to understand terms and then shows how marketers can utilize the psychology of personalization with A.I. to both create more effective marketing campaigns as well as increase customer loyalty. Pearson shows companies how to avoid Adobe’s warning of not using industrial-age technology in the digital era. Pearson also reveals how to create a platform of technology that seamlessly integrates EDW and real-time streaming data with social media content. Analytical models and neural nets can then be built on both commerical and open source technology to better understand the customer, thereby strengthening the brand and, just as importantly, increasing ROI. |
| data lake technology stack: Analytics and Big Data for Accountants Jim Lindell, 2020-11-03 Why is big data analytics one of the hottest business topics today? This book will help accountants and financial managers better understand big data and analytics, including its history and current trends. It dives into the platforms and operating tools that will help you measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators. Key topics covered include: Evidence-based techniques for finding or generating data, selecting key performance indicators, isolating program effects Relating data to return on investment, financial values, and executive decision making Data sources including surveys, interviews, customer satisfaction, engagement, and operational data Visualizing and presenting complex results |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
