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  2. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. [26] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale. [27]

  3. Data management - Wikipedia

    en.wikipedia.org/wiki/Data_management

    However, data has staged a comeback with the popularisation of the term big data, which refers to the collection and analyses of massive sets of data. While big data is a recent phenomenon, the requirement for data to aid decision-making traces back to the early 1970s with the emergence of decision support systems (DSS).

  4. Lambda architecture - Wikipedia

    en.wikipedia.org/wiki/Lambda_architecture

    The two view outputs may be joined before presentation. The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce. [1] Lambda architecture depends on a data model with an append-only, immutable data source that serves as a system of record.

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Features retinopathy grade and risk of macular edema 1200 Images, Text Classification, Segmentation 2008 [278] [279] Messidor Project Liver Disorders Dataset Data for people with liver disorders. Seven biological features given for each patient. 345 Text Classification 1990 [280] [281] Bupa Medical Research Ltd. Thyroid Disease Dataset

  6. Big data maturity model - Wikipedia

    en.wikipedia.org/wiki/Big_Data_Maturity_Model

    The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge. [6] Maturity stages. The different stages of maturity in the TDWI BDMM can be summarized as follows: Stage 1: Nascent. The nascent stage as a pre–big data environment. During this stage:

  7. Continuous analytics - Wikipedia

    en.wikipedia.org/wiki/Continuous_analytics

    Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price. The continuous approach runs multiple stateless engines which concurrently enrich, aggregate, infer and act on the data.

  8. Analytical skill - Wikipedia

    en.wikipedia.org/wiki/Analytical_skill

    Research involves the collection and analysis of information and data with the intention of founding new knowledge and/or deciphering a new understanding of existing data. [42] Research ability is an analytical skill as it allows individuals to comprehend social implications. [ 40 ]

  9. List of big data companies - Wikipedia

    en.wikipedia.org/wiki/List_of_big_data_companies

    Alpine Data Labs, an analytics interface working with Apache Hadoop and big data; AvocaData, a two sided marketplace allowing consumers to buy & sell data with ease. Azure Data Lake is a highly scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud