{"product_id":"data-engineering-for-beginners-paperback-softback-9781394325412","title":"Data Engineering for Beginners (Paperback \/ softback) 9781394325412","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Engineering for Beginners\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eChisom Nwokwu (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394325412, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 6 November 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e22.9 x 18.3 x 2.5 cm, 0.748 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eA hands-on technical and industry roadmap for aspiring data engineers\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eData Engineering for Beginners,\u003c\/i\u003e big data expert Chisom Nwokwu delivers a beginner-friendly handbook for everyone interested in the fundamentals of data engineering. Whether you're interested in starting a rewarding, new career as a data analyst, data engineer, or data scientist, or seeking to expand your skillset in an existing engineering role, Nwokwu offers the technical and industry knowledge you need to succeed. \u003c\/p\u003e\n\u003cp\u003eThe book explains: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eDatabase fundamentals, including relational and noSQL databases\u003c\/li\u003e \u003cli\u003eData warehouses and data lakes\u003c\/li\u003e \u003cli\u003eData pipelines, including info about batch and stream processing\u003c\/li\u003e \u003cli\u003eData quality dimensions\u003c\/li\u003e \u003cli\u003eData security principles, including data encryption\u003c\/li\u003e \u003cli\u003eData governance principles and data framework\u003c\/li\u003e \u003cli\u003eBig data and distributed systems concepts\u003c\/li\u003e \u003cli\u003eData engineering on the cloud\u003c\/li\u003e \u003cli\u003eEssential skills and tools for data engineering interviews and jobs\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Engineering for Beginners\u003c\/i\u003e offers an easy-to-read roadmap on a seemingly complicated and intimidating subject. It addresses the topics most likely to cause a beginning data engineer to stumble, clearly explaining key concepts in an accessible way. You'll also find: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eA comprehensive glossary of data engineering terms\u003c\/li\u003e \u003cli\u003eCommon and practical career paths in the data engineering industry\u003c\/li\u003e \u003cli\u003eAn introduction to key cloud technologies and services you may encounter early in your data engineering career\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for practicing and aspiring data analysts, data scientists, and data engineers, \u003ci\u003eData Engineering for Beginners\u003c\/i\u003e is an effective and reliable starting point for learning an in-demand skill. It's a powerful resource for everyone hoping to expand their data engineering Skillset and upskill in the big data era.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xxi\u003c\/p\u003e \u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Understanding Data 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Brief History of Data 2\u003c\/p\u003e \u003cp\u003eData in 19,000 bce: The Great Baboon and Abacus 2\u003c\/p\u003e \u003cp\u003eData in the 1600s: Public Health Statistics 2\u003c\/p\u003e \u003cp\u003eData in the 1800s: The U.S. Census 3\u003c\/p\u003e \u003cp\u003eData in the 1900s: The Concept of Storage 3\u003c\/p\u003e \u003cp\u003eData in the 1990s: Data and the Internet 4\u003c\/p\u003e \u003cp\u003eTypes of Data 4\u003c\/p\u003e \u003cp\u003eStructured Data 4\u003c\/p\u003e \u003cp\u003eUnstructured Data 5\u003c\/p\u003e \u003cp\u003eSemi-structured Data 6\u003c\/p\u003e \u003cp\u003eWhy Is Data Important? 7\u003c\/p\u003e \u003cp\u003eHealthcare 7\u003c\/p\u003e \u003cp\u003eSupply Chain 8\u003c\/p\u003e \u003cp\u003eTransportation and Logistics 8\u003c\/p\u003e \u003cp\u003eArtificial Intelligence 9\u003c\/p\u003e \u003cp\u003eData and Information 9\u003c\/p\u003e \u003cp\u003eSummary 10\u003c\/p\u003e \u003cp\u003eNotes 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Introduction to Data Engineering 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Engineering Explained Using an Oil Refinery Analogy 14\u003c\/p\u003e \u003cp\u003eAn Overview of the Data Engineering Life Cycle 15\u003c\/p\u003e \u003cp\u003eData Storage 16\u003c\/p\u003e \u003cp\u003eData Ingestion 20\u003c\/p\u003e \u003cp\u003eData Transformation 21\u003c\/p\u003e \u003cp\u003eData Serving 22\u003c\/p\u003e \u003cp\u003eNavigating Project Requirements, Engaging Stakeholders, and Delivering Business Value 24\u003c\/p\u003e \u003cp\u003eRequirements Gathering 24\u003c\/p\u003e \u003cp\u003eUnderstanding Stakeholders 24\u003c\/p\u003e \u003cp\u003eUnderstanding System Requirements 26\u003c\/p\u003e \u003cp\u003eDelivering Business Value 28\u003c\/p\u003e \u003cp\u003eThe Current State of Data Engineering 28\u003c\/p\u003e \u003cp\u003eThe Importance of Data Engineering 29\u003c\/p\u003e \u003cp\u003eSummary 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Database Fundamentals 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKey Concepts of Databases 34\u003c\/p\u003e \u003cp\u003eRows 34\u003c\/p\u003e \u003cp\u003eColumns 34\u003c\/p\u003e \u003cp\u003eSchema 35\u003c\/p\u003e \u003cp\u003eKeys 35\u003c\/p\u003e \u003cp\u003eTypes of Databases 35\u003c\/p\u003e \u003cp\u003eRelational Databases 36\u003c\/p\u003e \u003cp\u003eNoSQL Databases 47\u003c\/p\u003e \u003cp\u003eChoosing Between Relational and NoSQL Databases 55\u003c\/p\u003e \u003cp\u003eStart With Your Data’s Structure 55\u003c\/p\u003e \u003cp\u003eThink About the Relationships in Your Data 55\u003c\/p\u003e \u003cp\u003eHow Fast Do You Need to Move? 55\u003c\/p\u003e \u003cp\u003eHow Do You Need to Query Your Data? 55\u003c\/p\u003e \u003cp\u003eScaling and Performance 56\u003c\/p\u003e \u003cp\u003eTransaction and Strong Consistency Needs 56\u003c\/p\u003e \u003cp\u003eSummary 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 SQL Fundamentals 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to SQL 60\u003c\/p\u003e \u003cp\u003eBasic SQL Clauses 60\u003c\/p\u003e \u003cp\u003eComparison Operators 62\u003c\/p\u003e \u003cp\u003eLIKE Statement 63\u003c\/p\u003e \u003cp\u003eIN Statement 64\u003c\/p\u003e \u003cp\u003eBETWEEN Statement 64\u003c\/p\u003e \u003cp\u003eAND Statement 65\u003c\/p\u003e \u003cp\u003eOR Statement 65\u003c\/p\u003e \u003cp\u003eNOT Statement 66\u003c\/p\u003e \u003cp\u003eIS NULL and IS NOT NULL Statements 66\u003c\/p\u003e \u003cp\u003eSorting and Limiting 67\u003c\/p\u003e \u003cp\u003eAggregate Functions 68\u003c\/p\u003e \u003cp\u003eSum() 69\u003c\/p\u003e \u003cp\u003eAvg() 69\u003c\/p\u003e \u003cp\u003eMAX() and MIN() 69\u003c\/p\u003e \u003cp\u003eGroup by 70\u003c\/p\u003e \u003cp\u003eHaving 71\u003c\/p\u003e \u003cp\u003eUnderstanding Joins 72\u003cbr\u003e Inner Join 72\u003c\/p\u003e \u003cp\u003eLeft Join 73\u003c\/p\u003e \u003cp\u003eRight Join 74\u003c\/p\u003e \u003cp\u003eFull Outer Join 75\u003c\/p\u003e \u003cp\u003eSubqueries 76\u003c\/p\u003e \u003cp\u003eCommon Table Expressions (CTEs) 77\u003c\/p\u003e \u003cp\u003eSet Operations 78\u003c\/p\u003e \u003cp\u003eWindow Functions 80\u003c\/p\u003e \u003cp\u003eLab: Setting Up SQL Server and Running SQL Queries 85\u003c\/p\u003e \u003cp\u003eBest Practices for Writing Efficient SQL Queries 87\u003c\/p\u003e \u003cp\u003eSummary 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Database Design 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Modeling 92\u003c\/p\u003e \u003cp\u003eWhy Do We Need to Model Data? 92\u003c\/p\u003e \u003cp\u003eTypes of Data Modeling 93\u003c\/p\u003e \u003cp\u003eNormalization 100\u003c\/p\u003e \u003cp\u003eRules of Normalization 102\u003c\/p\u003e \u003cp\u003eDownsides of Normalization 109\u003c\/p\u003e \u003cp\u003eDenormalization 110\u003c\/p\u003e \u003cp\u003eData Modeling Best Practices 111\u003c\/p\u003e \u003cp\u003eDefine the Grain 111\u003c\/p\u003e \u003cp\u003eNormalize Now, Denormalize Later 112\u003c\/p\u003e \u003cp\u003eChoose the Right Data Types 112\u003c\/p\u003e \u003cp\u003eProper Naming Conventions 113\u003c\/p\u003e \u003cp\u003eDatabase Optimization 114\u003c\/p\u003e \u003cp\u003eIndexing 114\u003c\/p\u003e \u003cp\u003ePartitioning 115\u003c\/p\u003e \u003cp\u003eSharding 116\u003c\/p\u003e \u003cp\u003eViews 118\u003c\/p\u003e \u003cp\u003eSummary 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Data Warehouses, Data Lakes, and Data Lakehouses 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Warehouses 124\u003c\/p\u003e \u003cp\u003eExtract, Transform, and Load (ETL) 126\u003c\/p\u003e \u003cp\u003eSchema Design 127\u003c\/p\u003e \u003cp\u003eSnowflake Schema 132\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimensions 134\u003c\/p\u003e \u003cp\u003eData Marts 138\u003c\/p\u003e \u003cp\u003eBenefits of a Data Mart 138\u003c\/p\u003e \u003cp\u003eChallenges with Data Marts 138\u003c\/p\u003e \u003cp\u003eData Lakes 139\u003c\/p\u003e \u003cp\u003eHow Do Data Lakes Work? 139\u003c\/p\u003e \u003cp\u003eChallenges of Data Lakes 142\u003c\/p\u003e \u003cp\u003eData Lakehouse 142\u003c\/p\u003e \u003cp\u003eFeatures of a Data Lakehouse 143\u003c\/p\u003e \u003cp\u003eData Lakehouse Architecture 143\u003c\/p\u003e \u003cp\u003eThe Key Differences Between a Database, Data Warehouse, Data Lake, and Data Lakehouse 144\u003c\/p\u003e \u003cp\u003eSummary 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Data Pipelines 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBatch Pipelines 148\u003c\/p\u003e \u003cp\u003eComponents of a Batch Pipeline 148\u003c\/p\u003e \u003cp\u003eETL Pipelines vs. ELT Pipelines 151\u003c\/p\u003e \u003cp\u003eStream Pipelines 152\u003c\/p\u003e \u003cp\u003eHow Would This Work? 152\u003c\/p\u003e \u003cp\u003eComponents of a Streaming Data Pipeline 153\u003c\/p\u003e \u003cp\u003eLambda Architecture 164\u003c\/p\u003e \u003cp\u003eComponents of the Lambda Architecture 165\u003c\/p\u003e \u003cp\u003eAdvantages of the Lambda Architecture 166\u003c\/p\u003e \u003cp\u003eChallenges and Trade-offs 166\u003c\/p\u003e \u003cp\u003eData Orchestration 167\u003c\/p\u003e \u003cp\u003eDirected Acyclic Graphs (DAGs) 168\u003c\/p\u003e \u003cp\u003eScheduling and Automation 170\u003c\/p\u003e \u003cp\u003eMonitoring 171\u003c\/p\u003e \u003cp\u003eAlerts 172\u003c\/p\u003e \u003cp\u003eLab: Building an ETL Pipeline and Automating with Apache Airflow 173\u003c\/p\u003e \u003cp\u003eRequirements 174\u003c\/p\u003e \u003cp\u003eSet Up Your Development Environment 174\u003c\/p\u003e \u003cp\u003eExtracting Data from CSV 176\u003c\/p\u003e \u003cp\u003eTransforming the Data 177\u003c\/p\u003e \u003cp\u003eLoad the New CSV File into a Postgres Database Instance 181\u003c\/p\u003e \u003cp\u003eSchedule ETL Pipeline with Apache Airflow 182\u003c\/p\u003e \u003cp\u003eSummary 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Data Quality 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBad Data 188\u003c\/p\u003e \u003cp\u003eDimensions of Data Quality 190\u003c\/p\u003e \u003cp\u003eAccuracy 191\u003c\/p\u003e \u003cp\u003eCompleteness 191\u003c\/p\u003e \u003cp\u003eConsistency 194\u003c\/p\u003e \u003cp\u003eValidity 195\u003c\/p\u003e \u003cp\u003eUniqueness 196\u003c\/p\u003e \u003cp\u003eTimeliness 198\u003c\/p\u003e \u003cp\u003eAccessibility 198\u003c\/p\u003e \u003cp\u003eRelevance 198\u003c\/p\u003e \u003cp\u003eData Quality Hierarchy 199\u003c\/p\u003e \u003cp\u003eData Quality Best Practices 200\u003c\/p\u003e \u003cp\u003eSummary 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Data Security 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Data Security? 204\u003c\/p\u003e \u003cp\u003eCommon Threats to Data Security 205\u003c\/p\u003e \u003cp\u003eCore Principles of Data Security 206\u003c\/p\u003e \u003cp\u003eConfidentiality 206\u003c\/p\u003e \u003cp\u003eIntegrity 207\u003c\/p\u003e \u003cp\u003eAvailability 208\u003c\/p\u003e \u003cp\u003eData Encryption 209\u003c\/p\u003e \u003cp\u003eSymmetric Encryption 209\u003c\/p\u003e \u003cp\u003eAsymmetric Encryption 210\u003c\/p\u003e \u003cp\u003eData Masking 211\u003c\/p\u003e \u003cp\u003eUnderstanding Network Security 214\u003c\/p\u003e \u003cp\u003eAccess Control 216\u003c\/p\u003e \u003cp\u003eAuthentication 217\u003c\/p\u003e \u003cp\u003eAuthorization 219\u003c\/p\u003e \u003cp\u003eThe Principle of Least Privilege 222\u003c\/p\u003e \u003cp\u003eAccess Levels 224\u003c\/p\u003e \u003cp\u003eSecrets Management 225\u003c\/p\u003e \u003cp\u003eData Security and Data Privacy 225\u003c\/p\u003e \u003cp\u003eSummary 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Data Governance 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow to Think About Data Governance 230\u003c\/p\u003e \u003cp\u003eData Governance Framework 232\u003c\/p\u003e \u003cp\u003ePolicies 233\u003c\/p\u003e \u003cp\u003eRegulatory Compliance Policy 234\u003c\/p\u003e \u003cp\u003eData Classification Policy 238\u003c\/p\u003e \u003cp\u003eData Retention and Disposal Policy 239\u003c\/p\u003e \u003cp\u003eData Sharing Policy 240\u003c\/p\u003e \u003cp\u003eProcesses 241\u003c\/p\u003e \u003cp\u003eMetadata Management 242\u003c\/p\u003e \u003cp\u003eData Lineage 244\u003c\/p\u003e \u003cp\u003eIncident Management 244\u003c\/p\u003e \u003cp\u003eMaster Data Management 246\u003c\/p\u003e \u003cp\u003eRoles in the Data Governance Framework 247\u003c\/p\u003e \u003cp\u003eData Owner 248\u003c\/p\u003e \u003cp\u003eData Steward 248\u003c\/p\u003e \u003cp\u003eData Custodian 249\u003c\/p\u003e \u003cp\u003eChief Data Officer (CDO) 249\u003c\/p\u003e \u003cp\u003eData Management and Data Governance 250\u003c\/p\u003e \u003cp\u003eSummary 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Big Data and Distributed Systems 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Five V’s of Big Data 254\u003c\/p\u003e \u003cp\u003eVolume 255\u003c\/p\u003e \u003cp\u003eVelocity 255\u003c\/p\u003e \u003cp\u003eVariety 255\u003c\/p\u003e \u003cp\u003eVeracity 256\u003c\/p\u003e \u003cp\u003eValue 256\u003c\/p\u003e \u003cp\u003eDistributed Systems 256\u003c\/p\u003e \u003cp\u003eScalability 258\u003c\/p\u003e \u003cp\u003eFault Tolerance 259\u003c\/p\u003e \u003cp\u003eReliability 260\u003c\/p\u003e \u003cp\u003eConcurrency 260\u003c\/p\u003e \u003cp\u003eResource Management 260\u003c\/p\u003e \u003cp\u003eConsistency 261\u003c\/p\u003e \u003cp\u003eAvailability 261\u003c\/p\u003e \u003cp\u003eLoad Balancing 261\u003c\/p\u003e \u003cp\u003eLatency 262\u003c\/p\u003e \u003cp\u003eDistributed Data Processing 262\u003c\/p\u003e \u003cp\u003eApache Hadoop 262\u003c\/p\u003e \u003cp\u003eBig Data File Types 272\u003c\/p\u003e \u003cp\u003eAvro 272\u003c\/p\u003e \u003cp\u003eParquet 273\u003c\/p\u003e \u003cp\u003eOptimized Row Columnar (ORC) 274\u003c\/p\u003e \u003cp\u003eChoosing the File Type 275\u003c\/p\u003e \u003cp\u003eSummary 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Data Engineering on the Cloud 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCloud Computing 280\u003c\/p\u003e \u003cp\u003eOn-Premises 281\u003c\/p\u003e \u003cp\u003eCloud 281\u003c\/p\u003e \u003cp\u003eMaking the Right Choice 282\u003c\/p\u003e \u003cp\u003eCore Cloud Concepts 282\u003c\/p\u003e \u003cp\u003eStorage 282\u003c\/p\u003e \u003cp\u003eCompute 286\u003c\/p\u003e \u003cp\u003eNetworking 287\u003c\/p\u003e \u003cp\u003eCloud Service Models 291\u003c\/p\u003e \u003cp\u003eInfrastructure as a Service 291\u003c\/p\u003e \u003cp\u003ePlatform as a Service 292\u003c\/p\u003e \u003cp\u003eSoftware as a Service 293\u003c\/p\u003e \u003cp\u003eChoosing Between IaaS, PaaS, and SaaS 294\u003c\/p\u003e \u003cp\u003eA Hybrid Approach 298\u003c\/p\u003e \u003cp\u003eCloud Management Models 298\u003c\/p\u003e \u003cp\u003eServerless 299\u003c\/p\u003e \u003cp\u003eManaged 300\u003c\/p\u003e \u003cp\u003eSelf-Managed 301\u003c\/p\u003e \u003cp\u003ePutting It All Together 302\u003c\/p\u003e \u003cp\u003eCost Optimization 302\u003c\/p\u003e \u003cp\u003eUnderstanding Cloud Pricing Models 302\u003c\/p\u003e \u003cp\u003eRightsizing Resources 303\u003c\/p\u003e \u003cp\u003eSmart Job Scheduling 304\u003c\/p\u003e \u003cp\u003eStorage Optimization 304\u003c\/p\u003e \u003cp\u003eShutting Down Idle Resources 304\u003c\/p\u003e \u003cp\u003eUse Serverless Where Possible 304\u003c\/p\u003e \u003cp\u003eMonitoring and Alerting 305\u003c\/p\u003e \u003cp\u003eSummary 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Building a Career in Data Engineering 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Data Engineering Roles 308\u003c\/p\u003e \u003cp\u003eTypes of Data Engineers 308\u003c\/p\u003e \u003cp\u003ePlatform Data Engineer 308\u003c\/p\u003e \u003cp\u003eAnalytics Data Engineer 310\u003c\/p\u003e \u003cp\u003eAI\/ML Data Engineers 310\u003c\/p\u003e \u003cp\u003eLanding Your First Data Engineering Role 312\u003c\/p\u003e \u003cp\u003eA Typical Data Engineering Job Description 312\u003c\/p\u003e \u003cp\u003eHow to Build a Winning Résumé 314\u003c\/p\u003e \u003cp\u003ePreparing for a Data Engineering Interview 316\u003c\/p\u003e \u003cp\u003eThinking Like a Data Engineer 321\u003c\/p\u003e \u003cp\u003eThink in Systems 321\u003c\/p\u003e \u003cp\u003eLearn to Prioritize Data Quality 321\u003c\/p\u003e \u003cp\u003eDesign for Failure 321\u003c\/p\u003e \u003cp\u003eBalance Business Context with Technical Choices 322\u003c\/p\u003e \u003cp\u003eOptimize for Clarity, Then Speed 322\u003c\/p\u003e \u003cp\u003eThink Beyond the Tool 322\u003c\/p\u003e \u003cp\u003eMaster Automation 322\u003c\/p\u003e \u003cp\u003eSummary 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Sample Interview Questions 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSQL 325\u003c\/p\u003e \u003cp\u003eData Modeling 328\u003c\/p\u003e \u003cp\u003eData Pipelines 330\u003c\/p\u003e \u003cp\u003eApache Spark 332\u003c\/p\u003e \u003cp\u003eSystem Design 333\u003c\/p\u003e \u003cp\u003eData Engineering Glossary 335\u003c\/p\u003e \u003cp\u003eIndex 347\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer networking \u0026amp; communications [\u003ca title=\"See our other books on Computer networking \u0026amp; communications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20networking%20\u0026amp;%20communications%20%5BUT%5D%22\"\u003eUT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52462995177752,"sku":"9781394325412","price":41.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394325412.jpg?v=1785544637","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-engineering-for-beginners-paperback-softback-9781394325412","provider":"Freshly Printed Books","version":"1.0","type":"link"}