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Big Data Architecture Framework

An architecture framework establishes a common practice for creating, interpreting, analyzing and using architecture descriptions within a particular domain of application or stakeholder community (ISO/IEC/IEEE 42010). The Big Data Architecture Framework (BDAF) is an architecture framework for Big Data solutions, aimed at helping manage a set of discrete artifacts and implementing a collection of specific design elements. BDAF enforces the adherence to a consistent design approach, reduce the system complexity, enhance loose-coupling, maximize reuse, decrease the dependencies, and increase productivity.

BDAF comprises four integral parts: Domain-specific, Enablement-dependent, Platform-agnostic, and Technology-neutral (DEPT).
  • Domain-specific model: a verticalized instantiation tailored to an associated industry sector or line of business
  • Enablement-dependent model: a tiered stack with physical realizations of loosely-coupled building blocks, typically implemented by COTS or open source packages
  • Platform-agnostic model: a contextual grouping of functional blocks to represent the key business capabilities and operations
  • Technology-neutral model: a multi-layer logical structure that is independent from particular technologies

The BDAF components are model-centric and architecture-driven, forming a cohesive construct for Big Data processing, including data extract, ingestion, store, processing, schema, aggregation, messaging, interfacing, reporting, visualization, monitoring, streaming, and automation. Best practices and usage guides have been developed for practitioners to apply this comprehensive framework in real-life projects with ease.
 

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More Stories By Tony Shan

Tony Shan works as a senior consultant, advisor at a global applications and infrastructure solutions firm helping clients realize the greatest value from their IT. Shan is a renowned thought leader and technology visionary with a number of years of field experience and guru-level expertise on cloud computing, Big Data, Hadoop, NoSQL, social, mobile, SOA, BI, technology strategy, IT roadmapping, systems design, architecture engineering, portfolio rationalization, product development, asset management, strategic planning, process standardization, and Web 2.0. He has directed the lifecycle R&D and buildout of large-scale award-winning distributed systems on diverse platforms in Fortune 100 companies and public sector like IBM, Bank of America, Wells Fargo, Cisco, Honeywell, Abbott, etc.

Shan is an inventive expert with a proven track record of influential innovations such as Cloud Engineering. He has authored dozens of top-notch technical papers on next-generation technologies and over ten books that won multiple awards. He is a frequent keynote speaker and Chair/Panel/Advisor/Judge/Organizing Committee in prominent conferences/workshops, an editor/editorial advisory board member of IT research journals/books, and a founder of several user groups, forums, and centers of excellence (CoE).

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