Technological requirements of big data.

Storage: Big data requires the capacity to manage and store massive volumes of data, which often entails the usage of distributed storage systems like Hadoop HDFS or NoSQL databases.

Processing: A key requirement of big data is the capacity to quickly and effectively process and analyse enormous volumes of data. Usually, distributed processing solutions like Apache Spark or Apache Flink are used for this.

Data ingestion: It is essential to be able to gather and transmit data from diverse sources into the big data architecture in an efficient and effective manner.

Data integration: Since big data frequently consists of data from several sources with data in various forms and structures, the capacity to integrate and normalise data from various sources is another crucial necessity.

Data visualisation: Making sense of the data and sharing insights with stakeholders depend on your ability to display and convey the outcomes of big data research effectively.

Big data must be secure and private since they frequently include sensitive information, therefore doing so is essential.

Scalability: Another crucial necessity is the big data infrastructure's capacity to develop to meet the rising demands of data growth.

Some of the major technology prerequisites for big data are listed above. Organisations often need a mix of technology, software, and services that are especially made to handle the special problems of big data in order to analyse and utilise big data efficiently.

 

6 Big Data Software Requirements - Treehouse Tech Group

 

Apache Spark vs Flink, a detailed comparison (macrometa.com)

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