Ontotext Graphdb In Practice
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Ontotext GraphDB in Practice
"Ontotext GraphDB in Practice" "Ontotext GraphDB in Practice" delivers a comprehensive, hands-on guide to leveraging the full power of semantic graph databases for modern enterprise use. Beginning with the fundamentals of graph data modeling and the W3C Semantic Web stack, the book offers a clear exposition of RDF, knowledge graphs, and the capabilities that set Ontotext GraphDB apart in a fast-evolving data landscape. It explores real-world use cases across publishing, pharma, and cultural heritage, while imparting a critical understanding of the challenges and opportunities inherent to large-scale graph-based systems. This book equips data architects, engineers, and decision-makers with end-to-end expertise in deploying, configuring, and optimizing GraphDB in both standalone and clustered environments. Readers are guided through semantic data modeling, ontology management, robust data integration, and advanced SPARQL querying, with detailed attention to scalability, high availability, and security. Practical recipes, architectural best practices, and proven deployment patterns help organizations harness flexible interoperability with cloud, data lakes, streaming platforms, BI tools, and AI-driven analytics. Expertly structured to address both foundational knowledge and advanced operational disciplines, "Ontotext GraphDB in Practice" emphasizes enterprise integration, compliance, governance, and the relentless pursuit of data quality. Drawing on case studies and field-tested techniques, it illuminates the path from effective graph modeling to deploying mission-critical knowledge graphs that transform how businesses unify, analyze, and act on information. This book is an indispensable reference for building resilient, future-proof semantic data solutions at scale.
The Practice of Enterprise Modeling
This book constitutes the proceedings of the 17th IFIP Working Conference on the Practice of Enterprise Modeling, PoEM 2024, which took place in Stockholm, Sweden, during December 3-5, 2024. PoEM offers a forum for sharing experiences and knowledge between the academic community and practitioners from industry and the public sector. This year the theme of the conference is Industry 5.0 and Society 5.0. The 17 full papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named as follows: Enterprise modeling for digital transformation and industry applications; advances in enterprise modelling techniques; process mining and business process analysis; security, compliance, and configuration in enterprise modeling.
Data Engineering Best Practices
Author: Richard J. Schiller
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-10-11
Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.