Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization
Mobile internet data has the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs an efficient data processing model to provide support for data services, and, on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause risks in user privacy protection. This monograph focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, fine-grained data access control through effective user privacy protection mechanisms, and data sharing in the mobile internet.
If you're developing ABAP applications, you need CDS expertise. This book is your all-in-one guide, updated for SAP S/4HANA 2023! Start by learning to create and edit CDS views. Walk through CDS syntax and see how to define associations and annotations. Further refine your model by implementing access controls, service bindings, and table functions. Understand the CDS-based virtual data model, and then follow step-by-step instructions to model analytical and transactional applications. From modeling to testing to troubleshooting, this is the only book you need! In this book, you'll learn about: a. Creating Data Models Create data models using CDS! Learn the ins and outs of CDS view syntax, from key fields to joins to data types and beyond. Define associations to establish relationships, use annotations to enrich your logic, implement conditional logic to control access, and more. b. Modeling Applications See how CDS views support a new application architecture. Model analytical applications by defining analytical views and queries; then use CDS for transactional applications based on the ABAP RESTful application programming model. c. Extending, Testing, and Troubleshooting Put the finishing touches on your CDS data models. Extend and enhance CDS views and transactional models, develop automated tests using the test double framework, and troubleshoot common problems. Highlights include: 1) Data modeling 2) Application modeling 3) Associations 4) Annotations 5) Access controls 6) Business services 7) SAP HANA functions 8) Virtual data model 9) Analytical and transactional applications 10) Hierarchies 11) Extensibility 12) Testing and troubleshooting
The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use
Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment. In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap. This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands.
