Big data analytics Definitions

  • A

    advanced analytics

    While the traditional analytical tools that comprise basic business intelligence (BI) examine historical data, tools for advanced analytics focus on forecasting future events and behaviors

  • Apache Spark

    Apache Spark is an open source parallel processing framework for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads.

  • B

    backpropagation algorithm

    Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. Essentially, backpropagation is an algorithm used to calculate derivatives quickly.

  • big data analytics

    Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

  • business analytics (BA)

    Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis.

  • D

    data artist

    A data artist is a business analytics (BA) specialist who creates graphs, charts, infographics and other visual tools that help people understand complex data.

  • data preparation

    Data preparation is the process of aggregating and structuring data so that it can be used in business intelligence and analytics applications.

  • data sampling

    Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points in order to identify patterns and trends in the data set being examined as a whole.

  • data science as a service (DSaaS)

    Data science as a service (DSaaS) is the delivery of analytical reports and data products with embedded analytics produced by a service provider's team of data scientists to a client enterprise.

  • data scientist

    A data scientist is a professional responsible for collecting, analyzing and interpreting large amounts of data to identify ways to help a business improve operations and gain a competitive edge over rivals.

  • deep learning (deep neural networking)

    Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics.

  • E

    edge analytics

    Edge analytics applies algorithms to data at the point of collection in order to trigger actions and determine what should be sent back to a central data repository and what should be discarded.

  • ensemble modeling

    Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

  • H

    Hadoop cluster

    A Hadoop cluster is a special type of computational cluster designed specifically for storing and analyzing huge amounts of unstructured data in a distributed computing environment. 

  • Hadoop Distributed File System (HDFS)

    HDFS is a distributed file system that provides high-performance access to data across Hadoop clusters. Like other Hadoop-related technologies, HDFS has become a key tool for managing pools of big data and supporting big data analytics applications.

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