Big Data Platforms. ISCAS, May 2012 Liming Zhu & Xiwei Xu

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1 Big Data Platforms ISCAS, May 2012 Liming Zhu & Xiwei Xu

2 Architecture Research at NICTA Context: Architecting Dependable Applications Using Existing Platforms Business platforms Domains: finance, superannuation, logistics Characteristics: big data, software ecosystems... Cloud platforms and sel4 platforms Scope Architecting applications with underlying platform constraints Architecting evolvable platforms for unplanned applications Domain specific (not infrastructure/general) Dependability concerns: security, adaptability, availability Cost Effective: development time and operation time 2

3 Core Business Platforms What it is NOT about Use commodity platforms (OS/middleware/cloud/ERP) for supporting non-essential business hosting software products as services saving commodity IT cost What it is about Turn your core business into a public platform to let others build your killer apps (externalize dev.) mass/co-create/execute cross-org processes faster build a multi-sided biz ecosystem around your business use IT as opportunities and competitive advantage 3 NICTA Copyri

4 Platformisation Trend Platformisation is happening but only in selected domains User-generated content & social media (facebook/twitter) Software-as-a-Service (salesforce) Data mashups (Google Map) Infrastructure and development platforms (Amazon) Affiliation links & Online retail (Amazon) B2C Intention matching (Google AdWord) How can other businesses platformise? Finance, superannuation, logistics With new challenges from big data 4 NICTA Copyri

5 Big Data Platform Challenges Data sources Large, dynamic, real-time, and in-silos with uncertainty Unstructured, semi-structured, structured Domain-specific Ecosystem of data providers and consumers No central control Data processing and analysis Domain-specific tools used by multi-discipline teams Integration of analysis through complex pipelines With missing data and analysis provenance 5

6 Some Problems of Existing Solutions Assume standard or harmonized data schema possible Not possible even within a single organization Assume people could change their existing tools Large variety of domain specific analysis tools/methods One-size-fits-all solution from infrastructure-level MapReduce/Hadoop with ad hoc domain solutions Limited data access mechanisms Mostly through file dumps, not API Lacking flexible ways of moving analysis to data In higher level of abstraction and ways of existing analysis capabilities near data

7 Our Solution Treat data analysis pipelines as processes Similar to scientific workflow, but based on NICTA s Business Process Mashup (BPMashup) technology Web-based processes and Web API Following the REpresentational State Transfer (REST) style process fragments (analysis abstraction) exposed for coordination Dynamic next-steps (pointers to other analysis) returned Provenance, analysis status and analysis mashup logic exposed Some process fragments transmitted & executed at the edge Move analysis to data Current Limitation: incorporating of existing analysis tools Focus on API design and API-centric Architecture Media-type centric and layered approach Optimized for non-functional properties

8 For Big Data Providers Better platform API design for efficient access to data Evolvable and customizable for different and changing consumers and external apps and analysis Media-type centric and layered approach to API design New mechanism for accepting remote analysis to operate on local data Co-locating new domain-specific analysis engines with data + accepting remote analysis instructions Processing on the server or in the client (via Javascript) through BPMashup process fragments representing analysis 8

9 For Big Data Consumers New approach to integrating big data silos Better management and consumption of analysis provenance and pipelines RESTful approach to pipeline process management Follow next-step URLs to next data analysis New mechanism for tracking remote analysis Endpoints for exposing analysis status, provenance, result streams Treat each analysis step as a REST resource 9

10 For Provider/Consumer Ecosystem Many members of the ecosystem are both data providers and data consumers Value-chain Interdependency Architectures and Platforms Research Architecture principles, patterns, tactics for achieving certain quality for domain-specific big data platforms API-centric architecture Reusable architecture and platform components Cost-effective through better adaptability and efficiency 10

11 Some Collaboration Opportunities? Rework and ICSM applied to Platform API evolution and unplanned API usage Evolutionary development/requirements applied to Platform API design and evolution Empirical study on architecture-requirements relationships Platform design principles and its relationship to dependability Design as a micro-process Retrospection on design decisions Empirical study on design thinking? 11

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