Hander Martin (51 results)

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Paperback. Condition: new. Paperback. What is a Data Warehouse?A data warehouse is a centralized system designed to store, manage, and analyze large volumes of structured data collected from various sources within an organization. Unlike traditional databases that are optimized for day-to-day operations (like processing customer orders or updating records), data warehouses are optimized for analytical processing-helping businesses make sense of their data, identify trends, and support better decision-making.Think of a data warehouse as the digital brain of an organization's information ecosystem. It integrates data from multiple systems-such as customer relationship management (CRM), finance, sales, logistics, or marketing-into a single source of truth. This unified view allows analysts, executives, and data scientists to ask complex questions like: Which products are performing best in different regions?How has customer behavior changed over time?What forecasts can we make based on historical trends?Key Characteristics of a Data WarehouseTo understand what makes a data warehouse different from other data storage systems, it's essential to look at its core characteristics. These features define how a data warehouse functions and why it is uniquely suited for analytical tasks.Subject-OrientedA data warehouse is organized around key business subjects or domains-such as customers, sales, finance, or inventory-rather than around specific applications. This structure allows decision-makers to analyze data from a business perspective, making it easier to generate insights and answer high-level strategic questions.Example: Instead of storing data based on individual transactions, a subject-oriented warehouse might organize it by customer lifetime value, product performance, or regional sales trends. IntegratedData warehouses integrate data from various sources-often with differing formats, units, naming conventions, and data types-into a consistent, unified format. This integration ensures that data from different departments or systems (e.g., ERP, CRM, web analytics) can be analyzed together in a coherent way.Example: A customer's name might appear as "First Last" in one system and "Last, First" in another. A data warehouse standardizes these variations so that every occurrence of that customer is treated the same.Time-VariantUnlike operational systems that often only deal with current data, a data warehouse maintains historical data-sometimes spanning years. This time-oriented structure enables trend analysis, forecasting, and understanding how key metrics have evolved.Example: A business can compare this year's Q3 revenue with the last five years to detect seasonal patterns or long-term growth. Non-VolatileOnce data is loaded into the data warehouse, it is not changed or deleted. This ensures data stability, allowing for consistent reporting over time. Users can rely on the fact that historical reports remain accurate even if the source data changes in real-time systems.Example: If a product was sold at a certain price in 2021, that price remains in the warehouse even if the price changes later. This preserves historical accuracy. Optimized for Query and AnalysisUnlike transactional databases designed for fast inserts and updates, data warehouses are built for complex queries and analytics. They often include indexing, aggregation, and partitioning strategies that make it efficient to scan massive datasets.Example: A user can run a query to find the top 10 products by region over th Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: New. Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day.Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh.What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve.What You Will LearnDesign Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.Operate with observability and SRE practices using Snowflake usage views and SLOs.Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity.Who This Book Is ForData engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics).…

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Paperback. Condition: New. This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You'll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You'll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you'll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you'll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized-whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.…

Taschenbuch. Condition: Neu. Neuware -Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day.Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh.What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve.What You Will Learn- Design Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.- Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.- Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.- Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.- Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.- Operate with observability and SRE practices using Snowflake usage views and SLOs.- Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity.Who This Book Is ForData engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics). …

Paperback. Condition: New. This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You'll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You'll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you'll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you'll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized-whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.…

Taschenbuch. Condition: Neu. Neuware -This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale. 443 pp. Englisch.…

Taschenbuch. Condition: Neu. Neuware -This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale. 443 pp. Englisch.…

Taschenbuch. Condition: Neu. Neuware -This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You'll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You'll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you'll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you'll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimizedwhether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.…

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Taschenbuch. Condition: Neu. Neuware - Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day.Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh.What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve.What You Will LearnDesign Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.Operate with observability and SRE practices using Snowflake usage views and SLOs.Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity.Who This Book Is ForData engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics). …

Taschenbuch. Condition: Neu. Neuware - This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments.This book guides through the entire modern machine learning lifecycle. You ll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You ll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you ll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you ll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints.In the end, this book helps you build systems that are robust, auditable, and optimized whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era.What you will learn:Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples.Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques.Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks.Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments.Who this book is for:This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.…

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware -Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day.Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh.What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve.What You Will Learn- Design Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.- Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.- Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.- Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.- Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.- Operate with observability and SRE practices using Snowflake usage views and SLOs.- Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity.Who This Book Is ForData engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics).Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 556 pp. Englisch. …

Taschenbuch. Condition: Neu. Snowflake Data Warehouse Engineering | Architecture, Modeling, ELT Pipelines, and Operations | Martin Hander | Taschenbuch | xxxvii | Englisch | 2026 | Apress | EAN 9798868826276 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …