Rishu Raj (44 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware - Data engineering is the backbone of today's data-driven world. Whether you're preparing for a career in analytics, cloud computing, artificial intelligence, or big data, this comprehensive guide equips you with the knowledge and practical skills needed to design, build, and manage modern data platforms. Inside this book, you'll learn: - Data Engineering Fundamentals- Cloud Data Platforms (AWS, Azure, GCP)- SQL and Database Design- ETL & ELT Pipelines- Batch and Stream Processing- Apache Spark, Kafka, and Airflow- Data Warehousing and Lakehouse Architecture- Data Security and Governance- DevOps and DataOps- Real-World Industry Case Studies- Future Trends including AI and Generative AI- SQL Cheat Sheet for Quick RevisionPerfect for: - Students- Data Engineers- Software Engineers- Cloud Professionals- Data Analysts- AI and Machine Learning Practitioners- Technical Interview PreparationA practical, textbook-style guide designed to take you from beginner concepts to enterprise-level data engineering solutions.…

Taschenbuch. Condition: Neu. Neuware - Computational Biology is a comprehensive textbook designed to introduce readers to the principles, methods, and applications of modern computational biology. Combining biology, computer science, mathematics, and data science, this book provides a clear understanding of how computational approaches are transforming biological research and healthcare.The book is organized into 10 well-structured units, covering: - Introduction to Computational Biology- Molecular Biology Fundamentals- Biological Databases- Sequence Analysis- Genomics and Genome Analysis- Proteomics and Structural Bioinformatics- Phylogenetics and Evolutionary Analysis- Systems Biology and Network Biology- Artificial Intelligence and Machine Learning in Biology- Programming and Practical Computational Biology.…

Taschenbuch. Condition: Neu. Neuware - cloud professionals, data engineers, educators, and lifelong learners who strive to transform data into knowledge and innovation through Google Cloud technologies.

Taschenbuch. Condition: Neu. Neuware - Master one of the world's most powerful functional programming languages with this comprehensive guide to Haskell. Designed for students, self-learners, university courses, and software professionals, this book provides a structured journey from the fundamentals of functional programming to advanced software engineering techniques.Organized into 20 comprehensive units, the book covers everything you need to become proficient in Haskell, including recursion, higher-order functions, algebraic data types, type classes, polymorphism, modules, lazy evaluation, monads, applicative functors, concurrency, monad transformers, functional design patterns, and real-world application development.Each chapter includes clear explanations, practical examples, code demonstrations, chapter summaries, key terms, and review questions to reinforce learning and build confidence.…

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: new. Paperback. Data engineering is the backbone of today's data-driven world. Whether you're preparing for a career in analytics, cloud computing, artificial intelligence, or big data, this comprehensive guide equips you with the knowledge and practical skills needed to design, build, and manage modern data platforms. Inside this book, you'll learn: Data Engineering FundamentalsCloud Data Platforms (AWS, Azure, GCP)SQL and Database DesignETL & ELT PipelinesBatch and Stream ProcessingApache Spark, Kafka, and AirflowData Warehousing and Lakehouse ArchitectureData Security and GovernanceDevOps and DataOpsReal-World Industry Case StudiesFuture Trends including AI and Generative AISQL Cheat Sheet for Quick RevisionPerfect for: StudentsData EngineersSoftware EngineersCloud ProfessionalsData AnalystsAI and Machine Learning PractitionersTechnical Interview PreparationA practical, textbook-style guide designed to take you from beginner concepts to enterprise-level data engineering solutions. 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.

Taschenbuch. Condition: Neu. Neuware - Build Real-World Data Engineering Skills with PySpark and DatabricksModern data engineering is no longer just about writing SQL or moving files from one system to another. Today's data engineers need to build pipelines that are scalable, reliable, optimized, secure, observable, and ready for production.PySpark & Databricks Data Engineering provides a practical, structured path from foundational data engineering concepts to advanced enterprise-level data platforms.Starting with Python, Spark, and PySpark fundamentals, the book progressively takes you into advanced transformations, performance optimization, Delta Lake, incremental processing, CDC, Structured Streaming, Auto Loader, Databricks Workflows, Unity Catalog, security, governance, system design, and production troubleshooting.Inside this book, you will learn: - Data engineering fundamentals and architecture- Python concepts required for data engineering- PySpark DataFrames and Spark SQL- Transformations, actions, joins, aggregations, and window functions- Complex and nested data processing- Spark partitioning and shuffle- Broadcast joins and data-skew handling- Spark performance optimization- Databricks Lakehouse architecture- Delta Lake and ACID transactions- MERGE, UPDATE, DELETE, and time travel- Incremental data processing- Change Data Capture (CDC)- Structured Streaming- Auto Loader- Checkpointing and watermarks- Databricks Workflows and orchestration- Data quality and production reliability- Unity Catalog- Security and access control- Data lineage and governance- Data products and enterprise architecture- Monitoring, observability, and cost optimization- CI/CD and testing- Disaster recovery concepts- End-to-end data engineering projects- Production troubleshooting- Databricks system-design interviews- PySpark coding challenges. …

Taschenbuch. Condition: Neu. Neuware - Modern GitLab DevOps Handbook is a practical guide to designing and operating modern DevOps delivery pipelines using GitLab and the tools that commonly surround it.Starting with Git and GitLab fundamentals, the book progressively moves into CI/CD, GitLab Runners, automated testing, Docker, container registries, DevSecOps, Kubernetes, Terraform, GitOps, release engineering, monitoring, troubleshooting, and enterprise DevOps practices.A continuous project, DevOpsHub, connects the concepts and demonstrates how a real application can move from source code to a production-style environment.Inside, readers will explore: - Git and GitLab workflows- Merge Requests and code collaboration- GitLab CI/CD pipelines- GitLab Runners- Automated testing- Docker and container registries- DevSecOps practices- Kubernetes deployment- Terraform and Infrastructure as Code- GitOps workflows- Canary, rolling, and blue-green releases- Monitoring and observability- Production troubleshooting- Incident response and recovery- Enterprise GitLab practices- Real-world DevOps interview scenarios- Hands-on projects and challengesThe book focuses on practical understanding rather than memorizing isolated commands, helping readers build the knowledge needed to design, explain, troubleshoot, and improve modern DevOps environments.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Taschenbuch. Condition: Neu. Neuware - Databricks in Action: A Practical Guide to Data EngineeringModern data engineering is no longer just about moving data from one system to another. It is about building reliable, scalable, secure, and production-ready data platforms.Databricks in Action: A Practical Guide to Data Engineering provides a practical journey through the technologies and concepts that power modern data engineering. From Apache Spark and PySpark to SQL, Delta Lake, streaming, data quality, optimization, governance, and production deployment, this book focuses on the skills needed to build real-world data pipelines.Inside, readers will explore: - Data engineering fundamentals and modern lakehouse architecture- Apache Spark architecture and distributed processing- PySpark programming and DataFrame operations- Advanced SQL and data transformation techniques- Data ingestion and incremental processing- Bronze, Silver, and Gold data architectures- Delta Lake, MERGE, time travel, optimization, and maintenance- Batch and streaming data pipelines- CDC and slowly changing dimensions- Data quality, validation, and monitoring- Spark performance optimization and troubleshooting- Databricks CLI and REST API automation- Security, governance, and production deployment- End-to-end data engineering project practices- Interview questions and practical preparationThe book also includes detailed appendices covering SQL, PySpark, Spark functions, Delta Lake commands, Databricks CLI and REST API, troubleshooting, interview questions, and a complete project checklist.Whether you are learning data engineering, preparing for a Databricks-focused role, or looking for a practical reference while building modern data pipelines, this book is designed to help you develop the knowledge and confidence to work with Databricks in real-world environments.Learn the concepts. Build the pipelines. Troubleshoot the failures. Optimize the system. Think like a production data engineer.…

Taschenbuch. Condition: Neu. Neuware - Data Engineering Interview Mastery is a practical, scenario-driven guide designed to help aspiring and experienced Data Engineers prepare for technical interviews with confidence.Instead of relying on endless lists of memorized questions and answers, this book teaches readers how to think through problems, explain technical decisions, troubleshoot real-world failures, design scalable data platforms, and handle interviewer follow-up questions.The book covers the core skills expected in modern Data Engineering interviews, including SQL, Python, data modeling, ETL/ELT, Apache Spark, PySpark, Airflow, CDC, cloud data engineering, Kafka, streaming, data quality, system design, reliability, security, and cost optimization.Readers also get hands-on SQL, Python, and PySpark challenges, real-world scenarios, system-design exercises, mock interviews, senior-level questions, behavioral interview guidance, cheat sheets, and a 30-day preparation plan.Whether you're preparing for your first Data Engineering interview or targeting a senior-level role, this book is designed to take you from learning concepts to applying them under interview pressure.…

Taschenbuch. Condition: Neu. Neuware - Mastering SQL for Data Engineering is a comprehensive, hands-on guide that takes you from SQL fundamentals to advanced data engineering concepts used by leading technology companies. Whether you're a beginner or an experienced IT professional, this book provides practical explanations, real-world examples, and industry best practices to help you build scalable and efficient data solutions.Inside this book, you'll learn how to: - Build and manage relational databases- Write efficient SQL queries- Master joins, subqueries, and window functions- Optimize SQL performance using indexes and execution plans- Develop ETL and ELT data pipelines- Design modern data warehouses- Work with BigQuery, Snowflake, Redshift, Spark SQL, Hive, and Databricks- Build real-world data engineering projects- Prepare for SQL and Data Engineering interviewsWith practical examples, mini projects, and a capstone project, this book is an ideal resource for students, software engineers, database administrators, data analysts, and aspiring data engineers looking to succeed in today's cloud-driven data ecosystem.…

Paperback. Condition: new. Paperback. Master one of the world's most powerful functional programming languages with this comprehensive guide to Haskell. Designed for students, self-learners, university courses, and software professionals, this book provides a structured journey from the fundamentals of functional programming to advanced software engineering techniques.Organized into 20 comprehensive units, the book covers everything you need to become proficient in Haskell, including recursion, higher-order functions, algebraic data types, type classes, polymorphism, modules, lazy evaluation, monads, applicative functors, concurrency, monad transformers, functional design patterns, and real-world application development.Each chapter includes clear explanations, practical examples, code demonstrations, chapter summaries, key terms, and review questions to reinforce learning and build confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condition: Neu. Neuware - Master Data Science From Foundations to Modern AIData Science: Foundations, Systems & AI is a practical, end-to-end guide designed for learners who want to understand not only data science fundamentals, but also the modern AI systems transforming the industry.Starting with mathematics, statistics, Python, data preparation, and exploratory analysis, the book progressively moves into machine learning, advanced modeling, deep learning, NLP, foundation models, large language models, generative AI, RAG, AI agents, and production ML systems.You'll learn how to: - Understand and formulate real-world data problems- Work with Python and data-processing workflows- Apply statistics and machine learning- Build and evaluate predictive models- Understand deep learning and transformers- Work with NLP and embeddings- Build LLM and generative-AI applications- Design RAG systems- Understand AI agents and tool calling- Deploy and monitor ML models- Apply MLOps principles- Detect data and model drift- Design production AI systems- Prepare for data-science and ML interviews.…

Condition: New. Print on Demand.