Data Engineering with Python 2025: Build Scalable, Low-Latency Pipelines Using Modern Data Structures, Apache Arrow, and Advanced Algorithms

Chandler, Newman

ISBN 13: 9798289611697

Verlag: Independently published, 2025

Beschreibung

Unread book in perfect condition.

Bestandsnummer des Verkäufers 50406623

Bibliografische Details

• Titel: Data Engineering with Python 2025: Build Scalable, Low-Latency Pipelines Using Modern Data Structures, Apache Arrow, and Advanced Algorithms
• Verlag: Independently published
• Veröffentlichungsdatum: 2025
• Sprache: Englisch
• ISBN-13: 9798289611697
• Bindung: Softcover
• Zustand: As New

Über diesen Titel

Inhaltsangabe

Are your data pipelines buckling under volume spikes and latency demands? In an age where real-time insights separate leaders from laggards, you need a toolkit that scales with your ambition—and moves at the speed of your business.

Data Engineering with Python 2025 delivers a hands-on roadmap for building robust, low-latency pipelines using Python 3.12’s latest features, in-memory data structures, and high-performance libraries. From blazing-fast Apache Arrow serialization to advanced vectorized algorithms, this book shows you exactly how to architect and optimize pipelines that handle millions of events per second without breaking a sweat.

Inside, you’ll learn how to:

• Master Modern Data Structures: Choose between slotted dataclasses, NamedTuples, NumPy arrays, and Arrow tables to streamline memory and boost throughput.

• Implement Advanced Algorithms: Write recursive parsers, leverage PEP-709 comprehensions, and apply vectorized operations for blistering speed.

• Build Scalable Batch ETL: Orchestrate reliable workflows with Airflow or Prefect, transform data at scale with Pandas, Dask, and PySpark, and load into Redshift and BigQuery.

• Deploy Real-Time Streaming: Ingest with Kafka or Pulsar, maintain state with Flink or Spark Structured Streaming, and guarantee exactly-once processing across failures.

• Ensure Production Readiness: Profile memory and IPC performance, optimize hot code paths with Cython hints, and autoscale on Kubernetes or serverless to control costs.

• Maintain Visibility & Resilience: Integrate structured logging, Prometheus metrics, and OpenTelemetry tracing—and configure retry, idempotence, and alerting patterns that keep pipelines running smoothly.

Packed with clear, tutorial-style examples (no fluff, no filler), this book equips data engineers, architects, and DevOps professionals with the precise code and strategies needed to tackle 2025’s most demanding data challenges. Whether you’re architecting IoT telemetry feeds, financial tick processing, or clickstream analytics, you’ll emerge with the confidence to deliver high-performance, fault-tolerant systems that power real-time decisions.

Ready to transform your data infrastructure? Add Data Engineering with Python 2025 to your toolkit today and start building pipelines that outperform—and outlast—the competition.