Bigeye Data Quality Monitoring in Practice (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Bigeye Data Quality Monitoring in Practice" "Bigeye Data Quality Monitoring in Practice" is an in-depth guide to modern data quality assurance, designed for engineers, data leaders, and practitioners seeking to elevate trust and reliability in their data ecosystems. The book meticulously explores foundational principles such as accuracy, completeness, and consistency, drawing clear distinctions between legacy data profiling and contemporary, automated observability. It delves into the true business costs of poor data quality, highlights regulatory considerations, and unpacks the critical role...
"Bigeye Data Quality Monitoring in Practice" "Bigeye Data Quality Monitoring in Practice" is an in-depth guide to modern data quality assurance, designed for engineers, data leaders, and practitioners seeking to elevate trust and reliability in their data ecosystems. The book meticulously explores foundational principles such as accuracy, completeness, and consistency, drawing clear distinctions between legacy data profiling and contemporary, automated observability. It delves into the true business costs of poor data quality, highlights regulatory considerations, and unpacks the critical role of scalable, real-time monitoring in today's complex data architectures. The text navigates all facets of implementing Bigeye, a leading data observability platform, from architectural fundamentals and system integration to deployment strategies across cloud, on-premises, and hybrid environments. Readers will gain practical expertise in designing robust data quality monitors using statistical techniques, anomaly detection, and even machine learning, while managing scalability, performance, and cost. Additionally, the book demystifies alerting, incident management, and the integration of Bigeye with modern orchestration, warehousing, BI, and governance tools, enabling organizations to transform reactive fire-fighting into proactive, automated data reliability workflows. With a thorough examination of security, privacy, and compliance-including strategies for handling PII and aligning with standards such as SOC 2 and GDPR-the book provides actionable guidance for deploying trustworthy monitoring across enterprise environments. Real-world use cases illuminate patterns for cross-team collaboration, multi-tenancy, and demonstrating tangible ROI, while forward-looking chapters address innovations like AI-driven monitoring and autonomous, self-healing data pipelines. "Bigeye Data Quality Monitoring in Practice" offers both a blueprint and a visionary outlook for building resilient, compliant, and future-proof data platforms.
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