System Architecture Vs Data Architecture
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Data Architecture
Data is an expensive and expansive asset. Information hunger has forced retention capacity from megabytes to terabytes of data. Millions of dollars are spent accumulating data, and millions more are paid to the professional staff that nurture, secure, and extract information out of these billions of bytes of data. To ensure that it is usable, data must be structured in a flexible manner that is responsive to change, and is readily available for access. This book explains the principles underlying data architecture, how data evolves with organizations, the challenges organizations face in structuring and managing data, and the proven methods and technologies to solve these complex issues. The author takes a holistic approach to the field of data architecture from various applied perspectives, including data modeling, data quality, enterprise information management, database design, data warehousing, and data governance. Key Features Explains the fundamental concepts of enterprise architecture through definitions and real-world scenarios Teaches data managers and planners how to build a data architecture roadmap, structure the right team, and build a set of solutions for the various challenges they face Offers concise case studies that highlight how fundamental principles are put into practice.
DAMA-DMBOK
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.
Software Architecture for Big Data and the Cloud
Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors.