Big data analytics refers to the strategy of analyzing large volumes of data, or big data. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. The aim in analyzing all this data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights about the users who created it. Through this insight, businesses may be able to gain an edge over their rivals and make superior business decisions.
Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Traditional systems may fall short because they're unable to analyze as many data sources. It is written collaboratively and openly by a community of both actual and self-proclaimed experts who call themselves Wikipedians. It was created Jimmy Wales and Larry Sanger and was initially slated to be a for-profit website used to support Wales' and Sanger's earlier venture into online encyclopedia space.
Over the last five years, there has been a growing understanding of the role that Big Data can play in delivering priceless insights to an organization, revealing strengths and weaknesses and empowering companies to improve their practices. Big data has no agenda, is non-judgmental and non-partisan – it simply reveals a snapshot of activity.
The Apache Hive ™ data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive.
Apache Spark has as its architectural foundation the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way.[2] In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged[3] even though the RDD API is not deprecated.
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
(developed by Facebook) is a SQL engine that is lighting fast and reliable for reporting and ad-hoc analytics.
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