AUDIOBOOK
Data Engineering Design Patterns
Recipes For Solving The Most Common Data Engineering Problems
Bartosz Konieczny(0)
About
Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.
Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.
Throughout this journey, you'll use open source data tools and public cloud services to apply each pattern. You'll learn about challenges data engineers face and their impact on data systems; how these challenges relate to data system components; useful applications of data engineering patterns; how to identify and fix issues with your current data components; and technology-agnostic solutions to new and existing data projects, with open source implementation examples.
Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.
Throughout this journey, you'll use open source data tools and public cloud services to apply each pattern. You'll learn about challenges data engineers face and their impact on data systems; how these challenges relate to data system components; useful applications of data engineering patterns; how to identify and fix issues with your current data components; and technology-agnostic solutions to new and existing data projects, with open source implementation examples.