Technological Requirements of Big Data

 

For Big Data technologies to handle the huge quantities of endless data in today’s world there are three main technological requirements. These three requirements are storage, processing, and data integration.

Storage – The volume of data being produced is already at staggering level and is growing at an exponential rate. For Big Data to capture and analyse this data, the first challenge is storing it. Storage solutions have evolved along side Big Data to meet the ever-increasing demand for storage with the rise of the cloud being the most important factor for increasing the capacity of businesses and organisations to store their data and to keep costs down. Data Lakes provide scalable platforms for storing data in all its different varieties. (1)

Processing – With all that data being stored the next technological demand of Big Data technologies is processing power. As processing power has increased year on year, the ability to process huge amounts of data has increased in step. Software such as Hadoop and Spark have led the way in creating large scale software with the ability to analyse, process, and extract value from huge data sets. Machine learning has become the new way to extract value from Big Data evolving in response to new data input. (2)

Data Integration – Because of the volume and variety of data and data sources, the results of Big Data analyses can come in all different shapes and sizes. This is where the requirement for data integration comes in. Combining data from multiple sources into a single unified view is crucial for businesses and organisations to take advantage of these large data sets, gaining insights into business operations, customer behaviour, and market trends. (3)

 

1. https://cloud.google.com/learn/what-is-a-data-lake?hl=en

2. https://hevodata.com/learn/big-data-processing/

3. https://www.domo.com/glossary/what-is-data-integration

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