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Data Analytics – Preprocessing and Basics of Big Data

Learn about the preprocessing and basics of big data in Data Analytics.
Submitted by IncludeHelp, on December 25, 2021

Data Analytics

Data analytics is the act of inspecting the data with the purpose of finding insights.

Data is quantifiable information that may be utilized for mathematical computations and statistical analysis, with the goal of making real-world decisions based on the results of these mathematical derivations. The usage of quantitative data is used to answer queries such as "How many?", "How frequently?", and "How much?" Mathematical approaches can be used to verify and access this information, making it more convenient to use.

Data Preprocessing

Data preprocessing is a data mining technique that is used to turn raw data into a format that is useful and efficient for analysis.

Big Data

In the context of big data analytics, the application of advanced analytic techniques to extremely large and heterogeneous big data sets that contain structured, semi-structured, and unstructured data, from a variety of sources, and in various sizes ranging from terabytes to zettabytes is described.

Big data analytics is the process of examining enormous amounts of data in order to identify hidden patterns, correlations, and other information. Using today's technology, we can evaluate our data and obtain answers practically quickly - a process that would be much slower and less efficient if you used more traditional business intelligence tools.

We can use big data analytics to enable better and faster decision-making, modeling and forecasting of future consequences, and greater business intelligence. Open-source software such as Apache Hadoop, Apache Spark, and the full Hadoop ecosystem should be considered when developing a big data solution because they are cost-effective, versatile data processing and storage solutions that are built to handle the massive amount of data being generated today.

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