Bigeye Integrations for Data Quality Engineering (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Bigeye Integrations for Data Quality Engineering" "Bigeye Integrations for Data Quality Engineering" is an essential guide for modern data professionals seeking to engineer robust, production-grade data quality across today's increasingly complex ecosystems. This comprehensive resource explores the foundational principles of data quality engineering, delves into the architecture and observability mechanisms of the Bigeye platform, and offers nuanced frameworks for evaluating and implementing Bigeye within diverse data environments. From initial requirements gathering and platform comparison t...
"Bigeye Integrations for Data Quality Engineering" "Bigeye Integrations for Data Quality Engineering" is an essential guide for modern data professionals seeking to engineer robust, production-grade data quality across today's increasingly complex ecosystems. This comprehensive resource explores the foundational principles of data quality engineering, delves into the architecture and observability mechanisms of the Bigeye platform, and offers nuanced frameworks for evaluating and implementing Bigeye within diverse data environments. From initial requirements gathering and platform comparison through solution fit analysis, readers are equipped to make informed decisions regarding data quality strategies tailored to business, technical, and regulatory needs. The book methodically covers the integration of Bigeye with leading databases, data warehouses, ETL/ELT tools, and data orchestration platforms. Readers gain hands-on knowledge of secure access, schema discovery, partitioned data monitoring, and lineage capture, while also mastering integrations with polyglot data stacks. Advanced chapters address embedding monitors into workflows, handling pipeline failures, CI/CD automation, and orchestrating transactional or streaming data quality checks. Enterprise use cases are further enriched with best practices around alerting, incident management, regulatory compliance, and collaboration via integration with popular notification and ticketing systems. Aimed at architects, engineers, and data scientists, this book goes beyond technical depth to encompass governance, privacy, and extensibility-covering API usage, SDKs, plugin development, and the evolving landscape of ML and analytics integration. Special emphasis is placed on scaling, performance tuning, disaster recovery, and the future of data quality engineering, including cloud-native, serverless, and real-time paradigms. "Bigeye Integrations for Data Quality Engineering" stands as an authoritative reference for engineering trustworthy, scalable data pipelines in the enterprise.
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