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

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

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

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

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

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

Taschenbuch. Zustand: Neu. Neuware - Are your data pipelines becoming harder to manage, troubleshoot, and scale Do workflow failures, hidden dependencies, delayed schedules, and unreliable retries threaten the stability of your data platform Modern data systems are more connected than ever. Data warehouses, cloud services, APIs, machine learning workflows, analytics platforms, and event-driven applications all depend on reliable orchestration. The challenge is no longer running individual tasks, it is coordinating complex workflows across distributed environments while maintaining performance, visibility, and fault tolerance.Designing Workflow Orchestration Systems with Apache Airflow 3 provides a practical roadmap for building production-grade orchestration platforms using the latest capabilities of Airflow 3.x. Rather than focusing solely on DAG creation, this book teaches you how to design reliable, scalable, and maintainable workflow systems that support real business operations.Inside, you'll learn how to: - Build end-to-end data pipelines that remain reliable in production environments- Design maintainable DAGs with robust dependency management and safe re-runs- Implement fault-tolerant workflows using retries, backoff strategies, and self-healing patterns- Leverage dataset-aware scheduling, event-driven workflows, and API-based execution- Scale Airflow with Celery, Kubernetes, and distributed execution models- Monitor workflow health, collect meaningful metrics, and debug complex failures- Orchestrate ETL, ELT, analytics, and machine learning pipelines with confidence- Integrate Airflow with data warehouses, data lakes, APIs, and modern cloud platforms- Establish governance, security, CI/CD automation, and long-term operational excellenceFrom Designing DAGs for Reliability and Fault Tolerance and Failure Handling to Scaling Airflow in Production and Production Readiness and Operational Excellence, each chapter focuses on practical engineering decisions that directly impact system reliability and business outcomes.Whether you're a Data Engineer, Analytics Engineer, Platform Engineer, DevOps Professional, MLOps Practitioner, or Technical Architect, this book will help you move beyond basic scheduling and develop the skills needed to build resilient workflow orchestration systems at scale.If you're ready to coordinate complex dependencies, strengthen pipeline reliability, and deliver scalable production workflows with Apache Airflow 3.x, this book belongs on your desk. Get your copy now.…

Taschenbuch. Zustand: Neu. Neuware - Modern applications are no longer limited by code alone, they are limited by how fast they can process, distribute, and react to data in real time.Can your architecture handle millions of events without collapsing under latency, bottlenecks, consumer lag, or inconsistent data flows Designing Scalable Streaming Architectures with Apache Kafka is a practical, production-focused guide for data engineers, backend developers, software engineers, and solutions architects who want to build resilient, high-performance streaming systems that scale under real-world pressure.Instead of treating Apache Kafka as 'just another messaging tool,' this book explains how Kafka functions as the backbone of modern event-driven architecture, distributed systems, real-time analytics, microservices communication, and large-scale data pipelines.You'll learn how to: - Design scalable Kafka architectures for high-throughput systems- Structure events and schemas for long-term evolution- Build reliable producers and fault-tolerant consumers- Manage partitions, replication, and delivery guarantees- Diagnose consumer lag, rebalance issues, and production bottlenecks- Apply stream processing, CQRS, and event sourcing patterns effectively- Handle scaling, observability, and failure recovery in production environments- Understand the real trade-offs behind exactly-once processing and asynchronous communicationInside, you'll explore practical chapters such as Partitioning Strategies and Their Trade-Offs, Handling Failures and Backpressure in Applications, Multi-Cluster and Geo-Distributed Architectures, and When Not to Use Kafka: Limits and Trade-Offs.Every concept is explained with real architectural reasoning and hands-on code examples designed for modern distributed systems, not toy projects.Whether you are building real-time data pipelines, event-driven microservices, streaming analytics platforms, or scalable backend systems, this book will help you make better architectural decisions, avoid costly design mistakes, and operate Kafka with confidence in production.If you want to build faster, more resilient, and scalable streaming systems with Apache Kafka, this book belongs on your desk.…