Big Data Analytics

 

In my last post I discussed traditional statistics are their uses to derive value from data. In the examples I discussed in that previous post, the data sets were small and structured. This I what defines Big Data from traditional data. Because of the volume, variety, and velocity of data in the modern world, traditional data analysis techniques don’t work, and the field of Big Data analytics is formed around the analysis of large data sets.

Big Data analytics can be broken down into two categories, Decision-orientated and Action-orientated.

Decision-orientated – Focuses on analysing data to provide insights to support overall strategy and decision-making. Focusing on trends and patterns emerging from data to inform current strategies and predict possible future outcomes.

Action-orientated – Focuses on analysing data in real time to make immediate reactions with a focus on speed, reactivity and automation. Big Data technologies allow businesses to stay up-to-date and react to patterns emerging from real time data.

Hadoop and Spark are open-source frameworks designed for storing and processing large data sets. Hadoop is a cost-effective way to store and process massive datasets whilst Spark builds on this framework offering advantages over Hadoop in terms of processing speed and versatility.

Machine Learning has become a common tool now as the volume of data sets and need for real time analysis outpaces other methods. Training algorithms on large datasets to learn patterns, make predictions, and improve performances over time without being hard coded with updates has become an indispensable tool in modern Big Data.

Comments

  1. Dariusz Piotr GierejczykMay 8, 2025 at 2:58 AM

    I love statistics. You have shown the types of statistics in a very efficient and elegant way. BIG data rulez.

    ReplyDelete

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