Data Engineering - 15 Book Series (60 results)

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Paperback. Condition: New.

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Taschenbuch. Condition: Neu. Neuware - Stop Learning. Start Building. Become Job-Ready in Data Engineering.Data engineering is one of the fastest-growing careers in tech - but most resources teach theory, not real-world skills. This book is different.Instead of long explanations, you will build 12 complete, real-world data engineering projects using Python and SQL. Every project simulates a genuine business scenario, follows a professional structure, and gives you something tangible to show employers.What You Will BuildWorking through this book, you will create end-to-end data pipelines including CSV and file-based ingestion systems, API data pipelines with error handling, data cleaning and validation workflows, JSON to SQL transformation pipelines, a star schema data warehouse, reporting data marts for analytics, e-commerce and log processing pipelines, incremental loading systems, fault-tolerant and reliable pipelines, performance-optimized data workflows, and automated testing for data quality.Learn by Doing - Not Just ReadingEvery project follows the same professional structure: business problem, pipeline architecture, step-by-step implementation, working code, expected output, resume-ready bullet points, and interview questions. You won't just understand the concepts - you will know how to apply them.Become Job-ReadyThis book is designed to help you build a strong GitHub portfolio, write powerful resume project points, answer real data engineering interview questions with confidence, and develop the end-to-end thinking that employers are looking for. By the last page, you will have practical, demonstrable experience that sets you apart from other candidates.Who This Book Is ForThis book is written for beginners starting a career in data engineering, developers and software engineers transitioning into data roles, QA and automation engineers looking to upskill, and anyone who learns best through hands-on, project-based work. Basic knowledge of Python and SQL is all you need to get started.Why This Book Is DifferentNo unnecessary theory. No long academic explanations. No abstract concepts disconnected from practice. Only real projects, practical learning, and job-focused skills built through doing.Your Learning PathStart with simple file-based pipelines. Progress through API ingestion, data warehousing, and analytics systems. Finish with production-ready patterns including incremental loading, fault tolerance, performance optimization, and automated testing.By the End of This Book, You Will Have: Built 12 real-world data engineering projects. Developed end-to-end pipeline thinking. Created a job-ready portfolio. And gained the confidence to apply for data engineering roles - not someday, but now.…

Paperback. Condition: New.

Paperback. Condition: New.

Taschenbuch. Condition: Neu. Neuware - Master Apache Kafka the way working data engineers use it - with Python, real projects, and the judgement to match.Every modern data platform runs on event streams, and Kafka sits at the centre of nearly all of them. Yet most engineers meet Kafka through a copied tutorial and a producer that quietly loses messages. This book closes the gap between running a hello-world example and operating a reliable pipeline in production.A complete path, from first broker to full pipelines: - Part 1 - Kafka Fundamentals: architecture, KRaft mode, installation with Docker Compose, topics, partitions, offsets, and your first Python producers and consumers- Part 2 - Working with Kafka: CLI tools, Avro and Schema Registry, consumer groups in depth, replication and fault tolerance, exactly-once semantics, and performance tuning- Part 3 - Kafka for Data Engineers: streaming ETL, Kafka Connect, production-grade Python application structure, data quality and dead letter queues, real-time analytics, monitoring with Prometheus and Grafana, and security- Part 4 - Real Projects: four complete, runnable systems - an order processing pipeline, clickstream analytics with windowing, an IoT sensor pipeline, and a Debezium CDC pipelineBuilt for interviews as well as production. The final chapter delivers over 100 interview questions with model answers across architecture, reliability, ecosystem, security, scenario design, and behavioural rounds - plus three timed mock interviews and a career roadmap.Inside every chapter: - Runnable Python code using the confluent-kafka client- Architecture diagrams, comparison tables, and worked sizing examples- Best practices, common mistakes, and troubleshooting guidance drawn from production experience- Interview tips, practice exercises, and a five-question quiz with answersWho this book is for: beginner and junior data engineers, ETL developers, analysts moving into data engineering, software engineers adopting event-driven systems, students, and anyone preparing for data engineering interviews where Kafka appears in the job description - which today means nearly all of them.No prior Kafka experience is required. If you can write basic Python and run a Docker command, this book will take you the rest of the way - from your first topic to a monitored, secured, production-shaped pipeline you built yourself.…

Taschenbuch. Condition: Neu. Neuware - Build production-grade data engineering systems used in modern cloud environments - not just beginner ETL scripts.As organizations continue shifting toward cloud-native analytics, real-time processing, and distributed data platforms, the demand for skilled data engineers has never been higher. Yet most learning resources still focus on isolated concepts rather than showing how production systems are actually designed, built, and operated at scale.This book changes that.Advanced Data Engineering Projects with Python, SQL & Cloud is a hands-on, project-based guide that takes you beyond the basics and develops the practical engineering skills that modern data engineering roles demand. Every chapter is built around realistic production scenarios - not toy examples - using technologies widely adopted across the industry.What you will learn - Build scalable ETL and ELT pipelines using Python and SQL- Process large datasets with Apache Spark and PySpark- Design real-time streaming systems using Apache Kafka- Orchestrate complex workflows with Apache Airflow- Implement cloud-native architectures using AWS S3, Glue, Redshift, and Lambda- Build distributed analytics warehouses with performance-optimized table design- Create Bronze, Silver, and Gold Medallion data lakehouse architectures- Implement Change Data Capture and incremental loading strategies- Monitor pipeline health with logging, metrics, and automated alerting- Build automated data quality validation frameworks and production quality gates- Optimize distributed systems for scalability, performance, and cloud cost efficiency- Design complete enterprise-level data platforms from requirements to deploymentWho this book is for This book is written for aspiring data engineers building their first serious portfolio, ETL developers transitioning into cloud and big data platforms, backend engineers exploring distributed data systems, analytics engineers deepening their technical foundation, and professionals preparing for mid-level or senior data engineering interviews.Why this book is different Most technical books teach tools in isolation. This book teaches how modern systems work together - and more importantly, how to think like an engineer who builds them.You will learn not only how to use these technologies, but how to design for scalability, build for reliability, optimise for performance, and solve the kinds of problems that appear in real production environments at 2 a.m. when something breaks.By the end of this book, you will have a collection of advanced portfolio-quality projects, a strong foundation in enterprise architecture patterns, interview preparation material covering system design and production troubleshooting, and the practical engineering mindset that separates strong candidates in today's competitive data engineering job market.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Walk into your data engineering interview ready for every round.Most people don't fail data engineering interviews because they lack skill. They fail because no one showed them what each round is really testing, or how to answer under pressure. This book fixes that. Data Engineering Interview Mastery & System Design is a complete, end-to-end preparation guide that follows the real shape of a modern DE interview, from SQL and Python fundamentals to the senior system design rounds where the best offers are won and lost. What you'll master: - SQL interview problems: joins, window functions, CTEs, indexing, and query optimization- Python for data engineers: generators, decorators, OOP, concurrency, and 'what does this print ' questions- Warehousing & modeling: OLTP vs OLAP, star and snowflake schemas, slowly changing dimensions, and Data Vault- Lakes & lakehouses: Delta/Iceberg, ACID on files, schema evolution, and the medallion architecture- Big data & cloud: Spark internals, shuffles and data skew, Kafka delivery guarantees, AWS, and Airflow- System design: a repeatable framework plus four full designs - Batch ETL, Real-Time Streaming, Data Lakehouse, and a 5-billion-events/day Clickstream platform- Career mastery: resume, LinkedIn, portfolio projects, mock interviews, and salary negotiationWhy this book is different : Every key question follows a proven 8-part format - Question, Why Interviewers Ask, Simple Answer, Detailed Explanation, Real Business Example, Common Mistakes, Follow-up Questions, and Difficulty - so you don't just memorize an answer, you understand it and handle the follow-ups that actually decide the outcome. You also get 285+ rapid-fire questions in the appendices (Top 100 SQL, Top 50 Python, Top 50 Spark, Top 30 Kafka, Top 30 AWS, Top 25 System Design) and quick-reference cheat sheets for last-minute revision the night before. Who it's for : Freshers and career switchers, ETL/QA/analyst professionals moving into data engineering, software engineers crossing over, and experienced engineers targeting senior roles. Nobody is talked down to, and nothing is padded. This isn't a list of facts to memorize. It teaches you to think the way a data engineer thinks: clarify the problem, reason about how systems behave and fail, weigh the trade-offs, and say it all clearly out loud. The work ahead is real, and it's learnable. Scroll up, grab your copy, and start preparing the way the engineers who get hired do.…

Taschenbuch. Condition: Neu. Neuware - Crack Azure Data Engineer Interviews with Real Questions, Real Scenarios, and Real AnswersAzure Data Engineer interviews have changed. Panels no longer stop at definitions - they probe how you designed a pipeline, why you chose one Azure service over another, how you recovered a failed load at 2 a.m., and what your architecture costs the business every month. Azure Data Engineer Interview Mastery prepares you for exactly that kind of interview.What's Inside- 600+ interview questions - detailed worked answers, chapter quizzes, and a 300-question rapid revision pack- 100 scenario-based questions covering real production failure modes across twelve categories- 55+ comparison tables, 20+ architecture diagrams, and 35+ SQL, Python, and PySpark code examples- Five complete enterprise project walkthroughs - banking, retail, healthcare, e-commerce, and IoT- A full mock interview, HR and behavioural round preparation, and a rapid revision chapter for the night before your interviewFull Coverage of the Modern Azure Data StackAzure fundamentals and career roadmap - Azure Storage and ADLS Gen2 - Azure Data Factory (fundamentals and real project patterns) - Synapse Analytics architecture and performance tuning - Azure Databricks and Apache Spark - Microsoft Fabric and OneLake - SQL for data engineers (window functions, CTEs, MERGE, indexing, execution plans) - Python for data engineers - ETL and data modelling concepts (SCDs, star schemas, medallion architecture) - Cloud architecture patterns (Lambda, Kappa, data mesh, event-driven design) - Azure DevOps and CI/CD - Security and governance - Performance and cost optimisation.DP-203 Concepts ThroughoutEvery relevant chapter flags DP-203 exam-aligned concepts, so this book doubles as a certification companion alongside your interview preparation.Who This Book Is For- Freshers preparing for their first Azure Data Engineer interview- Data engineers, ETL developers, and BI developers moving to Azure- Cloud engineers and SQL developers broadening into data platforms- DP-203 learners who want interview-ready depth- Working professionals with up to ten years of experience targeting product companies, consultancies, and large enterprisesWritten in clear, practical, professional English by an author who has built production Azure data platforms across banking, retail, healthcare, e-commerce, and IoT - this is the most complete Azure Data Engineer interview book available today.Prepare once. Walk into every round with confidence. …

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - SQL for Data Engineering - ETL, Warehousing, Cloud Platforms & AI WorkflowsMost SQL books teach you to write queries. This one teaches you to build systems.If you know basic SQL but struggle to apply it in production environments, or you are preparing for data engineering interviews and want experience beyond simple SELECT statements, this book was written for you.SQL for Data Engineering bridges the gap between tutorial SQL and the skills that data engineering roles actually demand - ETL pipelines, warehouse design, query optimization, and the architectural thinking that separates engineers who build reliable data systems from those who only query them.What you will learn: - Advanced querying - joins, CTEs, subqueries, window functions, and analytical SQL patterns- Data transformation - cleaning, standardisation, safe casting, and deduplication techniques- ETL architecture - incremental loading, staging patterns, Slowly Changing Dimensions, and idempotent pipeline design- Warehouse design - star schemas, dimensional modelling, partitioning, indexing, and materialised views- Performance optimisation - EXPLAIN ANALYZE, execution plans, query tuning, and production-grade optimisation- Semi-structured data - JSON and JSONB processing, schema drift handling, and event pipeline patterns- Cloud platforms - Snowflake, BigQuery, Redshift, and Databricks with practical SQL for each- AI workflows - prompt engineering for SQL tasks, AI-assisted debugging, and the risks of generated codeFour industry projects you will build: - E-Commerce Analytics Warehouse- SaaS Subscription Metrics Platform- Banking Transaction Fraud Monitoring System- Food Delivery ETL and Operations PipelineEach project covers a realistic business problem, complete ETL pipeline, warehouse schema, KPI queries, and interview preparation - designed to go directly into your portfolio.You also get: - Integrated interview Q&A in every chapter drawn from real hiring panels- Resume bullet points after every chapter and project- A complete career roadmap for aspiring data engineers- Appendices covering SQL functions, data types, troubleshooting, glossary, and a tool landscape guide.…

Paperback. Condition: new. Paperback. Stop Learning. Start Building. Become Job-Ready in Data Engineering.Data engineering is one of the fastest-growing careers in tech - but most resources teach theory, not real-world skills. This book is different.Instead of long explanations, you will build 12 complete, real-world data engineering projects using Python and SQL. Every project simulates a genuine business scenario, follows a professional structure, and gives you something tangible to show employers.What You Will BuildWorking through this book, you will create end-to-end data pipelines including CSV and file-based ingestion systems, API data pipelines with error handling, data cleaning and validation workflows, JSON to SQL transformation pipelines, a star schema data warehouse, reporting data marts for analytics, e-commerce and log processing pipelines, incremental loading systems, fault-tolerant and reliable pipelines, performance-optimized data workflows, and automated testing for data quality.Learn by Doing - Not Just ReadingEvery project follows the same professional structure: business problem, pipeline architecture, step-by-step implementation, working code, expected output, resume-ready bullet points, and interview questions. You won't just understand the concepts - you will know how to apply them.Become Job-ReadyThis book is designed to help you build a strong GitHub portfolio, write powerful resume project points, answer real data engineering interview questions with confidence, and develop the end-to-end thinking that employers are looking for. By the last page, you will have practical, demonstrable experience that sets you apart from other candidates.Who This Book Is ForThis book is written for beginners starting a career in data engineering, developers and software engineers transitioning into data roles, QA and automation engineers looking to upskill, and anyone who learns best through hands-on, project-based work. Basic knowledge of Python and SQL is all you need to get started.Why This Book Is DifferentNo unnecessary theory. No long academic explanations. No abstract concepts disconnected from practice. Only real projects, practical learning, and job-focused skills built through doing.Your Learning PathStart with simple file-based pipelines. Progress through API ingestion, data warehousing, and analytics systems. Finish with production-ready patterns including incremental loading, fault tolerance, performance optimization, and automated testing.By the End of This Book, You Will Have: Built 12 real-world data engineering projects. Developed end-to-end pipeline thinking. Created a job-ready portfolio. And gained the confidence to apply for data engineering roles - not someday, but now. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Taschenbuch. Condition: Neu. Neuware - Master Data Engineering System Design - From Fundamentals to Real-World Production SystemsSystem design interviews trip up data engineers who are strong on execution but have never been shown how to structure a complete architectural answer. This book fixes that gap - and gives you the production knowledge to back it up.What you will learn: - How to approach any system design question using a six-step framework that works every time- The fundamentals of distributed systems: CAP theorem, replication, partitioning, consistency models, and message delivery guarantees- How to design batch pipelines, streaming pipelines, and CDC architectures from scratch- Modern data architectures: data warehouse (Kimball, Inmon, Medallion), data lake (Bronze/Silver/Gold), and lakehouse (Delta Lake, Iceberg, Hudi)- AWS, Azure, and GCP data services - and how to combine them into production-ready platforms- Five complete real-world case studies: Uber GPS platform, Netflix analytics, e-commerce data platform, real-time fraud detection, and an AI/ML platform with feature store and RAG- 20 most-asked system design interview questions with full answers, architectures, and common mistakes- Where data engineering is heading: AI-assisted pipelines, the real-time lakehouse, vector databases, and Data MeshWho this book is for: - Junior to mid-level data engineers preparing for system design interviews- Data engineering beginners who want to understand how components fit together into real systems- College students and freshers entering the data engineering field- Professionals moving from analytics or software engineering into data engineeringEvery chapter follows a consistent structure: core concepts, real-world examples, architecture diagrams, common mistakes, and interview questions. The writing is practitioner-level - no academic jargon, short paragraphs, and honest trade-off discussions throughout.This is a standalone book. No prior system design experience required - only a basic familiarity with SQL and Python.Topics covered: system design fundamentals - scalability - distributed systems - OLTP vs OLAP - data modeling - star schema - SCD Type 2 - storage formats - Parquet - Avro - Delta Lake - Apache Iceberg - batch pipelines - Airflow - streaming pipelines - Apache Kafka - Flink - CDC - Debezium - data warehouse - data lake - lakehouse - AWS - Azure - GCP - Redshift - Snowflake - BigQuery - feature store - RAG - vector databases - fraud detection - A/B testing - interview framework.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: New.

Paperback. Condition: new. Paperback. 150 Real Interview Questions. Answered the Way Strong Candidates Actually Answer Them.Most interview prep teaches you theory and hopes you can translate it under pressure. This book skips straight to the part that actually matters: the questions themselves, answered exactly the way a strong candidate would answer them in the room. Data Engineering Interview Questions & Real Interview Experiences is a pure question-and-answer companion - 150 frequently reported questions spanning SQL, Python, ETL/CDC, data warehousing, PySpark, Kafka, the modern cloud stack, and system design. No filler chapters. No theory to wade through first. Just the questions, drilled the way real preparation works. What's inside: Advanced SQL (25 questions): window functions, gaps and islands, recursive CTEs, indexing, query optimizationAdvanced Python (15 questions): generators, decorators, the GIL, memory management, processing huge datasetsETL / CDC / Data Quality (20 questions): reconciliation, idempotency, SCD Type 2 testing, schema drift, production troubleshootingData Warehouse & Modeling (15 questions): fact table grain, Kimball vs Inmon, Data Vault, bridge tables, conformed dimensionsPySpark Advanced (25 questions): shuffles, data skew, the Catalyst optimizer, broadcast joins, AQE, production tuningKafka & Streaming (15 questions): exactly-once semantics, consumer lag, watermarks, schema evolution, broker failoverAWS / Snowflake / Databricks / Airflow (20 questions): Glue vs EMR, Snowflake architecture, Delta Lake, Airflow DAG designSystem Design & Production Scenarios (15 questions): 10 full system designs (CDC pipelines, fraud detection, clickstream analytics, lakehouses) plus 5 real production-incident walkthroughsEvery question follows the same nine-part structure: Why interviewers ask it, the simple answer, the detailed answer, a real business example, production-quality code, an architecture diagram where it adds value, common mistakes that cost candidates the offer, the follow-up questions that come next, and key takeaways for fast revision. This consistent format is deliberate - by question fifty, you stop reading explanations and start pattern-matching, exactly the skill you need in the room. The production-scenario questions deserve special mention. A Spark job that suddenly takes nine times longer. Consumer lag that jumps from 5K to 2 million. Revenue that drops by half overnight. These are the questions that separate a good offer from a great one, and this book treats them as a first-class skill - not an afterthought. A note on company-attributed questions: where this book references well-known employers alongside a question, these are presented as representative interview questions frequently reported by candidates - reflecting publicly known topics and formats, never claimed as transcripts of any individual interview. Who it's for: Freshers and QA professionals transitioning into data engineering, ETL developers ready for a senior title, and working data engineers preparing for their next interview at a major tech, finance, or consulting employer. You already have the skill. This book gives you the rehearsal. Volume 2 in the Data Engineering Interview series - a companion to Data Engineering Interview Mastery & System Design. Stop studying theory. Start answering questions. Scroll up and grab your copy. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condition: new. Paperback. Walk into your data engineering interview ready for every round.Most people don't fail data engineering interviews because they lack skill. They fail because no one showed them what each round is really testing, or how to answer under pressure. This book fixes that. Data Engineering Interview Mastery & System Design is a complete, end-to-end preparation guide that follows the real shape of a modern DE interview, from SQL and Python fundamentals to the senior system design rounds where the best offers are won and lost. What you'll master: SQL interview problems: joins, window functions, CTEs, indexing, and query optimizationPython for data engineers: generators, decorators, OOP, concurrency, and "what does this print?" questionsWarehousing & modeling: OLTP vs OLAP, star and snowflake schemas, slowly changing dimensions, and Data VaultLakes & lakehouses: Delta/Iceberg, ACID on files, schema evolution, and the medallion architectureBig data & cloud: Spark internals, shuffles and data skew, Kafka delivery guarantees, AWS, and AirflowSystem design: a repeatable framework plus four full designs - Batch ETL, Real-Time Streaming, Data Lakehouse, and a 5-billion-events/day Clickstream platformCareer mastery: resume, LinkedIn, portfolio projects, mock interviews, and salary negotiationWhy this book is different?: Every key question follows a proven 8-part format - Question, Why Interviewers Ask, Simple Answer, Detailed Explanation, Real Business Example, Common Mistakes, Follow-up Questions, and Difficulty - so you don't just memorize an answer, you understand it and handle the follow-ups that actually decide the outcome. You also get 285+ rapid-fire questions in the appendices (Top 100 SQL, Top 50 Python, Top 50 Spark, Top 30 Kafka, Top 30 AWS, Top 25 System Design) and quick-reference cheat sheets for last-minute revision the night before. Who it's for?: Freshers and career switchers, ETL/QA/analyst professionals moving into data engineering, software engineers crossing over, and experienced engineers targeting senior roles. Nobody is talked down to, and nothing is padded. This isn't a list of facts to memorize. It teaches you to think the way a data engineer thinks: clarify the problem, reason about how systems behave and fail, weigh the trade-offs, and say it all clearly out loud. The work ahead is real, and it's learnable. Scroll up, grab your copy, and start preparing the way the engineers who get hired do. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Ready to move beyond the basics and build real-world Data Engineering systems?Databricks & Delta Lake: Part 2 takes you deeper into the technologies powering modern data platforms. Building upon the foundations of Databricks, SQL, and PySpark, this practical guide focuses on advanced concepts such as Delta Lake, Lakehouse Architecture, Structured Streaming, Kafka, AI-powered workflows, and production-grade data pipelines.Today's organizations require more than traditional ETL processes. They need scalable architectures, reliable data governance, real-time analytics, and intelligent systems capable of supporting analytics and AI at scale. This book shows you how these technologies work together in real-world environments and how professional Data Engineers design modern Lakehouse solutions.Inside this book, you will learn: Delta Lake fundamentals and ACID transactionsTime Travel, Transaction Logs, Schema Enforcement, and Schema EvolutionBuilding and managing Delta TablesMedallion Architecture (Bronze, Silver, Gold)Lakehouse Architecture and modern data platformsUnity Catalog for governance, permissions, and data sharingStructured Streaming and real-time data pipelinesApache Kafka integration with DatabricksDelta Live Tables for production ETL pipelinesAI capabilities in Databricks including Vector Search and AI FunctionsModern AI workflows, RAG concepts, and LLM-powered data systemsEnd-to-End E-Commerce Lakehouse ProjectReal-world architecture diagrams and production patternsInterview questions and career guidanceThis book is written for: - Data Engineers who want to master Databricks and Delta Lake- ETL Developers transitioning to modern cloud architectures- SQL and PySpark developers looking to expand their skills- Students preparing for Data Engineering careers- Intermediate professionals building real-world data pipelines- Anyone interested in Lakehouse Architecture and AI-powered analyticsUnlike books that focus only on theory, this guide emphasizes practical learning. Every chapter includes clear explanations, business scenarios, architecture diagrams, code examples, best practices, and common mistakes to avoid.You will explore how data flows from raw ingestion to curated business datasets, how streaming systems process events in real time, and how AI is transforming the future of Data Engineering.This book is Part 2 of the Databricks & Delta Lake series.Part 1 covers: - Modern Data Engineering Fundamentals- Databricks Workspace and Architecture- Databricks SQL- Apache Spark Fundamentals- PySpark DataFrames and Transformations- Performance Optimization TechniquesPart 2 builds on those concepts and takes you into advanced topics that are increasingly demanded in modern Data Engineering roles.By the end of this book, you will understand how to design scalable Lakehouse architectures, build reliable streaming pipelines, implement Delta Lake best practices, and leverage AI-powered workflows using Databricks.Whether you're preparing for your next Data Engineering role, upgrading your skills, or exploring the future of modern data platforms, this book provides the practical knowledge and hands-on guidance you need.Start building the next generation of data systems with Databricks and Delta Lake today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condition: new. Paperback. Build production-grade data engineering systems used in modern cloud environments - not just beginner ETL scripts.As organizations continue shifting toward cloud-native analytics, real-time processing, and distributed data platforms, the demand for skilled data engineers has never been higher. Yet most learning resources still focus on isolated concepts rather than showing how production systems are actually designed, built, and operated at scale.This book changes that.Advanced Data Engineering Projects with Python, SQL & Cloud is a hands-on, project-based guide that takes you beyond the basics and develops the practical engineering skills that modern data engineering roles demand. Every chapter is built around realistic production scenarios - not toy examples - using technologies widely adopted across the industry.What you will learn?Build scalable ETL and ELT pipelines using Python and SQLProcess large datasets with Apache Spark and PySparkDesign real-time streaming systems using Apache KafkaOrchestrate complex workflows with Apache AirflowImplement cloud-native architectures using AWS S3, Glue, Redshift, and LambdaBuild distributed analytics warehouses with performance-optimized table designCreate Bronze, Silver, and Gold Medallion data lakehouse architecturesImplement Change Data Capture and incremental loading strategiesMonitor pipeline health with logging, metrics, and automated alertingBuild automated data quality validation frameworks and production quality gatesOptimize distributed systems for scalability, performance, and cloud cost efficiencyDesign complete enterprise-level data platforms from requirements to deploymentWho this book is for?This book is written for aspiring data engineers building their first serious portfolio, ETL developers transitioning into cloud and big data platforms, backend engineers exploring distributed data systems, analytics engineers deepening their technical foundation, and professionals preparing for mid-level or senior data engineering interviews.Why this book is different?Most technical books teach tools in isolation. This book teaches how modern systems work together - and more importantly, how to think like an engineer who builds them.You will learn not only how to use these technologies, but how to design for scalability, build for reliability, optimise for performance, and solve the kinds of problems that appear in real production environments at 2 a.m. when something breaks.By the end of this book, you will have a collection of advanced portfolio-quality projects, a strong foundation in enterprise architecture patterns, interview preparation material covering system design and production troubleshooting, and the practical engineering mindset that separates strong candidates in today's competitive data engineering job market. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. SQL for Data Engineering - ETL, Warehousing, Cloud Platforms & AI WorkflowsMost SQL books teach you to write queries. This one teaches you to build systems.If you know basic SQL but struggle to apply it in production environments, or you are preparing for data engineering interviews and want experience beyond simple SELECT statements, this book was written for you.SQL for Data Engineering bridges the gap between tutorial SQL and the skills that data engineering roles actually demand - ETL pipelines, warehouse design, query optimization, and the architectural thinking that separates engineers who build reliable data systems from those who only query them.What you will learn: Advanced querying - joins, CTEs, subqueries, window functions, and analytical SQL patternsData transformation - cleaning, standardisation, safe casting, and deduplication techniquesETL architecture - incremental loading, staging patterns, Slowly Changing Dimensions, and idempotent pipeline designWarehouse design - star schemas, dimensional modelling, partitioning, indexing, and materialised viewsPerformance optimisation - EXPLAIN ANALYZE, execution plans, query tuning, and production-grade optimisationSemi-structured data - JSON and JSONB processing, schema drift handling, and event pipeline patternsCloud platforms - Snowflake, BigQuery, Redshift, and Databricks with practical SQL for eachAI workflows - prompt engineering for SQL tasks, AI-assisted debugging, and the risks of generated codeFour industry projects you will build: E-Commerce Analytics WarehouseSaaS Subscription Metrics PlatformBanking Transaction Fraud Monitoring SystemFood Delivery ETL and Operations PipelineEach project covers a realistic business problem, complete ETL pipeline, warehouse schema, KPI queries, and interview preparation - designed to go directly into your portfolio.You also get: Integrated interview Q&A in every chapter drawn from real hiring panelsResume bullet points after every chapter and projectA complete career roadmap for aspiring data engineersAppendices covering SQL functions, data types, troubleshooting, glossary, and a tool landscape guide This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.