Big data platforms: What's next?

Borkar, Vinayak; Carey, Michael J.; Li, Chen

doi:10.1145/2331042.2331057

Cited by 65 publications

(15 citation statements)

References 9 publications

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“…General technical considerations are found in [66] and [80]. The Big Data management system ASTERIX is introduced in [81]. A value chain for Big Data is conceptualized in [70].…”

Section: Related Workmentioning

confidence: 99%

Towards a Framework for Enterprise Architecture Analytics

Wißotzki

Jugel

Möhring³

et al. 2014

2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations

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Abstract-Current approaches for enterprise architecture lack analytical instruments for cyclic evaluations of business and system architectures in real business enterprise system environments. This impedes the broad use of enterprise architecture methodologies. Furthermore, the permanent evolution of systems desynchronizes quickly model representation and reality. Therefore we are introducing an approach for complementing the existing top-down approach for the creation of enterprise architecture with a bottom approach. Enterprise Architecture Analytics uses the architectural information contained in many infrastructures to provide architectural information. By applying Big Data technologies it is possible to exploit this information and to create architectural information. That means, Enterprise Architectures may be discovered, analyzed and optimized using analytics. The increased availability of architectural data also improves the possibilities to verify the compliance of Enterprise Architectures. Architectural decisions are linked to clustered architecture artifacts and categories according to a holistic EAM Reference Architecture with specific architecture metamodels. A special suited EAM Maturity Framework provides the base for systematic and analytics supported assessments of architecture capabilities.

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“…General technical considerations are found in [66] and [80]. The Big Data management system ASTERIX is introduced in [81]. A value chain for Big Data is conceptualized in [70].…”

Section: Related Workmentioning

confidence: 99%

Towards a Framework for Enterprise Architecture Analytics

Wißotzki

Jugel

Möhring³

et al. 2014

2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations

View full text Add to dashboard Cite

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“…Due to the increased needs to scale-up databases to data volumes that exceeded processing and/or storage capabilities, systems that ran on computer clusters started to emerge. Perhaps the first milestone took place in June 1986 when Teradata [6] used the first parallel database system (hardware and software), with one terabyte storage capacity, in Kmart data warehouse to have all their business data saved and available for relational queries and business analysis [7,8] . Other examples include the Gamma system of the University of Wisconsin [9] and the GRACE system of the University of Tokyo [10] .…”

Section: Introductionmentioning

confidence: 99%

Big data analytics for wireless and wired network design: A survey

et al. 2018

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a b s t r a c tCurrently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions.In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks' control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial effort s toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

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“…While these are indispensable tools for data center-scale processing tasks, and the open source community continues to make big improvements such as the Stinger Initiative [48], they still lack much of the efficiency and feature set of established data warehousing technology. But most importantly, they require significant engineering effort to roll out and use [16].…”

Section: Introductionmentioning

confidence: 99%

The Snowflake Elastic Data Warehouse

Dageville¹,

Cruanes²,

Żukowski³

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

186

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We live in the golden age of distributed computing. Public cloud platforms now offer virtually unlimited compute and storage resources on demand. At the same time, the Software-as-a-Service (SaaS) model brings enterprise-class systems to users who previously could not afford such systems due to their cost and complexity. Alas, traditional data warehousing systems are struggling to fit into this new environment. For one thing, they have been designed for fixed resources and are thus unable to leverage the cloud's elasticity. For another thing, their dependence on complex ETL pipelines and physical tuning is at odds with the flexibility and freshness requirements of the cloud's new types of semi-structured data and rapidly evolving workloads. We decided a fundamental redesign was in order. Our mission was to build an enterprise-ready data warehousing solution for the cloud. The result is the Snowflake Elastic Data Warehouse, or "Snowflake" for short. Snowflake is a multi-tenant, transactional, secure, highly scalable and elastic system with full SQL support and built-in extensions for semi-structured and schema-less data. The system is offered as a pay-as-you-go service in the Amazon cloud. Users upload their data to the cloud and can immediately manage and query it using familiar tools and interfaces. Implementation began in late 2012 and Snowflake has been generally available since June 2015. Today, Snowflake is used in production by a growing number of small and large organizations alike. The system runs several million queries per day over multiple petabytes of data. In this paper, we describe the design of Snowflake and its novel multi-cluster, shared-data architecture. The paper highlights some of the key features of Snowflake: extreme elasticity and availability, semi-structured and schema-less data, time travel, and end-to-end security. It concludes with lessons learned and an outlook on ongoing work.

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Big data platforms: What's next?

Abstract: Three computer scientists from UC Irvine address the question "What's next for big data?" by summarizing the current state of the big data platform space and then describing ASTERIX, their next-generation big data management system.

Cited by 65 publications

References 9 publications

Towards a Framework for Enterprise Architecture Analytics

Towards a Framework for Enterprise Architecture Analytics

Big data analytics for wireless and wired network design: A survey

The Snowflake Elastic Data Warehouse

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