Deequ for Scalable Data Quality Assurance (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Deequ for Scalable Data Quality Assurance" In an era where data powers decision-making at every level, ensuring the quality of massive and fast-growing datasets poses unprecedented challenges. "Deequ for Scalable Data Quality Assurance" addresses this critical need by exploring not only the evolving standards and requirements for data quality in large-scale, modern systems but also the profound business and technical risks of neglecting it. The book begins by framing the dimensions of data quality—accuracy, completeness, consistency, timeliness, and validity—and critically evaluates tradi...
"Deequ for Scalable Data Quality Assurance"
In an era where data powers decision-making at every level, ensuring the quality of massive and fast-growing datasets poses unprecedented challenges. "Deequ for Scalable Data Quality Assurance" addresses this critical need by exploring not only the evolving standards and requirements for data quality in large-scale, modern systems but also the profound business and technical risks of neglecting it. The book begins by framing the dimensions of data quality—accuracy, completeness, consistency, timeliness, and validity—and critically evaluates traditional approaches, making a compelling case for automation and scalable, data-driven architectures.
At the heart of this work is a comprehensive exploration of Deequ, an open-source library purpose-built for automated, scalable data quality checks on distributed platforms such as Apache Spark. Through clear architectural exposition, the book demystifies Deequ’s foundational abstractions—metrics, checks, constraints, and analyzers—then guides readers in designing expressive, reusable, and parameterized validations. Advanced chapters reveal how to extend Deequ with custom metrics, orchestrate robust quality workflows in production, and integrate with CI/CD, monitoring, and audit frameworks, all while upholding security and regulatory compliance in areas such as GDPR and HIPAA.
Drawing from hands-on case studies in enterprise environments, the book illustrates the end-to-end lifecycle of data quality management—from automated detection and remediation to storytelling with actionable insights. Readers gain practical knowledge in deployment strategies, visualization, and root cause analytics while also being introduced to future trends in automated quality assurance and intelligent profiling. Whether you are a data engineer, architect, or leader, this book is an essential guide to mastering scalable data quality in the era of big data.
In an era where data powers decision-making at every level, ensuring the quality of massive and fast-growing datasets poses unprecedented challenges. "Deequ for Scalable Data Quality Assurance" addresses this critical need by exploring not only the evolving standards and requirements for data quality in large-scale, modern systems but also the profound business and technical risks of neglecting it. The book begins by framing the dimensions of data quality—accuracy, completeness, consistency, timeliness, and validity—and critically evaluates traditional approaches, making a compelling case for automation and scalable, data-driven architectures.
At the heart of this work is a comprehensive exploration of Deequ, an open-source library purpose-built for automated, scalable data quality checks on distributed platforms such as Apache Spark. Through clear architectural exposition, the book demystifies Deequ’s foundational abstractions—metrics, checks, constraints, and analyzers—then guides readers in designing expressive, reusable, and parameterized validations. Advanced chapters reveal how to extend Deequ with custom metrics, orchestrate robust quality workflows in production, and integrate with CI/CD, monitoring, and audit frameworks, all while upholding security and regulatory compliance in areas such as GDPR and HIPAA.
Drawing from hands-on case studies in enterprise environments, the book illustrates the end-to-end lifecycle of data quality management—from automated detection and remediation to storytelling with actionable insights. Readers gain practical knowledge in deployment strategies, visualization, and root cause analytics while also being introduced to future trends in automated quality assurance and intelligent profiling. Whether you are a data engineer, architect, or leader, this book is an essential guide to mastering scalable data quality in the era of big data.
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