Dataiku Platform Foundations: Definitive Reference for Developers and Engineers

"Dataiku Platform Foundations"

Dataiku Platform Foundations offers a comprehensive guide to mastering the architectural, operational, and analytical core of the Dataiku Data Science Studio (DSS). Beginning with a detailed exploration of Dataiku’s modular architecture—including its processing engines, storage management, and system integration capabilities—this book equips readers with the foundational knowledge required to build scalable, resilient, and extensible data pipelines. Readers are led through sophisticated orchestration techniques, storage abstractions, high availability architectures, and extensibility mechanisms, ensuring a strong grasp of the platform’s technical underpinnings.

The book progresses into advanced data engineering, collaborative project management, and governance, providing practical insights into dataset handling, hybrid workflow creation, and large-scale transformation. It demystifies critical aspects such as automated profiling, lineage tracking, permission management, and regulatory compliance, all while emphasizing reproducibility and robust audit trails. Support for complex machine learning workflows is provided through chapters on feature engineering, model experimentation, interpretability, and deployment strategies—covering both automated and custom approaches to suit a range of analytic needs.

For practitioners focused on operational excellence, Dataiku Platform Foundations delves into best practices for deployment, MLOps integration, security, and extension. The text addresses CI/CD pipelines, resource orchestration with cloud and container technologies, incident management, and fine-grained security and compliance mechanisms. Closing with a vision for the future, the book explores emerging trends, hybrid and multi-cloud strategies, and the cultural imperatives of building data-driven organizations, ensuring professionals are well-prepared to leverage Dataiku as a catalyst for innovation and enterprise-wide analytics maturity.

© 2025 HiTeX Press (E-Book): 6610000817771

Erscheinungsdatum

E-Book: 30. Mai 2025