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  2. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    Big data was originally associated with three key concepts: volume, variety, and velocity. [3] The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Thus a fourth concept, veracity, refers to the quality or insightfulness of the data. [4]

  3. Big data maturity model - Wikipedia

    en.wikipedia.org/wiki/Big_Data_Maturity_Model

    The organization start to investigate big data analytics; Stage 3: Early adoption The "chasm" There is then generally a series of hurdles it needs to overcome. These ...

  4. Analytics - Wikipedia

    en.wikipedia.org/wiki/Analytics

    Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.

  5. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    Data analysis focuses on extracting insights and drawing conclusions from structured data, while data science involves a more comprehensive approach that combines statistical analysis, computational methods, and machine learning to extract insights, build predictive models, and drive data-driven decision-making. Both fields use data to ...

  6. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]

  7. Online analytical processing - Wikipedia

    en.wikipedia.org/wiki/Online_analytical_processing

    It can ingest data from offline data sources (such as Hadoop and flat files) as well as online sources (such as Kafka). Pinot is designed to scale horizontally. Mondrian OLAP server is an open-source OLAP server written in Java. It supports the MDX query language, the XML for Analysis and the olap4j interface specifications.

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