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Hadoopability

Hadoop is steadily pulling away from the crowded pack to become the preferred selection in Big Data implementations. Despite the dominance, not everything fits Hadoop in reality. How can we objectively assess the suitability of Hadoop to a business problem? We need a way to evaluate the match comprehensively.

Hadoopability is a measure to gauge the aptness of Hadoop in a business context. It helps analyze if Hadoop is a viable option and compare the pros and cons as a candidate. It is a qualitative and quantitative metrics to justify if it makes sense to Hadoopify an application with tradeoffs. In practice, Hadoopability is represented as a suitability score that address various data aspects to determine the applicability. The following are 25 key items in the metrical structure:
  • Amount: the magnitude of total data volume - scale of GB, TB, PB, EB
  • Type: structured, semi-structured, unstructured
  • Mode: batch, near real-time, real-time
  • Ingestion: streaming, non-event-driven
  • Locality: local, distributed, centralized
  • Representation: ER, normalized, denormalized
  • Access: SQL, NoSQL
  • Programming: low-level, mid-level, high-level
  • Processing: transactional, analytical
  • Compression: ratio, performance overheads
  • Serialization: read/write balance
  • Style: document-oriented, graph-centric
  • Latency: low, medium, high
  • Bound: memory, I/O, CPU, network
  • Transformability: conversion of formats
  • Hierarchy: part-whole, depth
  • Association: inter-element relationship, semantics
  • Security: regulatory, access control, compliance, privacy
  • Theorem: consistency, availability, partition
  • Principle: ACID, BASE
  • Hosting: physical, virtual, private/public/hybrid cloud
  • Quality: performance, SLA, reliability, retention, DR
  • Adoption: chasm, early taker, entrenching, mainstream, laggard, obsoleting
  • Support: commercial, vendor, community, forum, broker, standards, practices
  • Skillset: competency, training, retooling, constraints, resources, tools

The Hadoopability Assessment Scorecard (HAS) has been designed to rate the fitness and readiness in the comprehensive evaluation and analysis. Please reach out to the author for detailed features of this automated tool.

For more information, please contact Tony Shan ([email protected]).

Read the original blog entry...

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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