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Cost-Effective Data Pipelines (Balancing Trade-Offs When Developing Pipelines in the Cloud)

List Price: $65.99
SKU:
9781492098645
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  • Product Details

    Author:
    Sev Leonard
    Format:
    Paperback
    Pages:
    286
    Publisher:
    O'Reilly Media (August 22, 2023)
    Language:
    English
    ISBN-13:
    9781492098645
    ISBN-10:
    1492098647
    Dimensions:
    7" x 9.19"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251022163324-20251022.xml
    Folder:
    TWO RIVERS
    List Price:
    $65.99
    Case Pack:
    14
    As low as:
    $56.75
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    16.32oz
    Imprint:
    O'Reilly Media
  • Overview

    The low cost of getting started with cloud services can easily evolve into a significant expense down the road. That's challenging for teams developing data pipelines, particularly when rapid changes in technology and workload require a constant cycle of redesign. How do you deliver scalable, highly available products while keeping costs in check?

    With this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage. You'll also pick up best practices for code development, testing, and monitoring.

    By focusing on the entire design process, you'll be able to deliver cost-effective, high-quality products. This book helps you:

    • Reduce cloud spend with lower cost cloud service offerings and smart design strategies
    • Minimize waste without sacrificing performance by rightsizing compute resources
    • Drive pipeline evolution, head off performance issues, and quickly debug with effective monitoring
    • Set up development and test environments that minimize cloud service dependencies
    • Create data pipeline code bases that are testable and extensible, fostering rapid development and evolution
    • Improve data quality and pipeline operation through validation and testing