Big Data

Big data involves working with and developing insights from large datasets.

The key distinctions between regular data and big data are volume, velocity, and variety. Generally, big data is more extensive information with more individual components that are collected in a shorter period of time. Big data sources are often new but can encompass older data streams as well.

Examples

Big data is utilized across multiple industries and applied in many ways, including the following:

  • Marketing departments use this data for targeted advertising, promoting products, and services that align with company interests.
  • Healthcare professionals may track data such as heart rate and sleep habits to improve health surveillance and assist people with performing ADLs (activities of daily living).
  • The transportation and automobile industries may use big data to improve road safety and navigation, as well as take prevetative measures against inclement weather.

Processing Big Data

Because big data is so vast and comprehensive, it needs to be processed before being analyzed for insights. This involves collecting and comparing data from multiple sources, cleaning it to remove any errors or duplicates, and more.

After processing, data scientists go through the data to find any relevant patterns in the big data. This often involves the use of machine learning algorithms and data visualization methods for creating insights. Statistics also play a key role in data analysis as it seeks to explain the relationships between the data and probable outcomes.

Programming Languages

There are several programming languages used to collect, process, analyze, and visualize big data, including the following:

  • C and C++ still hold up as solid choices.
  • Java has big data tools that are open-source, flexible, and free to use.
  • JavaScript is ideal for building interactive web pages that share big data-generated information.
  • Python features many libraries that specialize in working with statistical anlysis and big data.
  • R excels at using statistical analysis and visualization to draw insightful and actionable conclusions.
  • SQL was developed for handling large databases with relationships between different variables from different datasets.

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