Isbn: 9798197727084 - python for data engineering: etl pipelines, warehousing & real-time streaming (5 resultados)

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  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798197727084

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    Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US

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    Condición: Nuevo

    EUR 33,69

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    Cantidad disponible: Más de 20 disponibles

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

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798197727084

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    Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK

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    Condición: Nuevo

    EUR 30,19

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

  • Idioma: Inglés

    Editorial: Independently Published Mai 2026, 2026

    9798197727084

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Condición: Nuevo

    EUR 41,49

    Envío por EUR 35,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 2 disponibles

    Taschenbuch. Condición: Neu. Neuware - What separates data pipelines that survive Friday night deployments from those that collapse before Monday morning Most Python developers can write scripts that process CSV files. Few can build systems that handle API timeouts, schema drift, and that 2 AM alert when the CFO's dashboard shows $40,000 in missing revenue. This book bridges that gap with fifteen years of battle-tested engineering wisdom drawn from hedge funds, health-tech startups, and Fortune 500 retailers.Inside, you will learn how to: - Build ETL pipelines that fail predictably, recover automatically, and alert meaningfully-before stakeholders notice- Design warehouse schemas that handle real-world data quality issues without requiring midnight refactoring- Implement real-time streaming with Kafka and Python that survives Unicode exceptions and rate-limit storms- Apply testing strategies and type safety that prevent the 3 AM debugging sessions every data engineer dreads- Control costs and observability across cloud infrastructure so your pipelines outlast your tenureEvery pattern here has been tested against actual latency requirements, budget constraints, and stakeholders who change specifications mid-quarter. The code is complete, runnable, and intentionally imperfect-showing the retry logic, workarounds, and memory optimizations that production demands.Data engineering is not about elegant syntax. It is about writing defensible systems that handle upstream failures, downstream schema changes, and the inevitable moment when someone uploads the wrong file at the worst possible time.If you are ready to move from writing scripts to owning infrastructure that powers business decisions, this manual gives you the frameworks, failure patterns, and operational rigor to do exactly that. Your pipelines will not just run. They will endure.Get your copy today and build data systems that last-starting with your very next deployment.…

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798197727084

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    Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books

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    Condición: Nuevo

    EUR 31,23

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    Cantidad disponible: Más de 20 disponibles

    Condición: New. Print on Demand.

  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798197727084

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    Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

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    Condición: Nuevo

    EUR 34,01

    Envío por EUR 43,65 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: 1 disponible

    Paperback. Condición: new. Paperback. What separates data pipelines that survive Friday night deployments from those that collapse before Monday morning?Most Python developers can write scripts that process CSV files. Few can build systems that handle API timeouts, schema drift, and that 2 AM alert when the CFO's dashboard shows $40,000 in missing revenue. This book bridges that gap with fifteen years of battle-tested engineering wisdom drawn from hedge funds, health-tech startups, and Fortune 500 retailers.Inside, you will learn how to: - Build ETL pipelines that fail predictably, recover automatically, and alert meaningfully-before stakeholders notice- Design warehouse schemas that handle real-world data quality issues without requiring midnight refactoring- Implement real-time streaming with Kafka and Python that survives Unicode exceptions and rate-limit storms- Apply testing strategies and type safety that prevent the 3 AM debugging sessions every data engineer dreads- Control costs and observability across cloud infrastructure so your pipelines outlast your tenureEvery pattern here has been tested against actual latency requirements, budget constraints, and stakeholders who change specifications mid-quarter. The code is complete, runnable, and intentionally imperfect-showing the retry logic, workarounds, and memory optimizations that production demands.Data engineering is not about elegant syntax. It is about writing defensible systems that handle upstream failures, downstream schema changes, and the inevitable moment when someone uploads the wrong file at the worst possible time.If you are ready to move from writing scripts to owning infrastructure that powers business decisions, this manual gives you the frameworks, failure patterns, and operational rigor to do exactly that. Your pipelines will not just run. They will endure.Get your copy today and build data systems that last-starting with your very next deployment. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…