Technological requirements of big data

 Technological requirements of big data

Big Data management cannot be efficient if there is no powerful technology ground to rely on while handling a great number of different and changeable data. A first and important step toward that is scalable storage where technologies like the Hadoop Distributed File System (HDFS) or cloud-based storage offerings (e.g., AWS, Google Cloud, Azure) are the most frequently chosen solutions for storing both structured and unstructured data. In like manner, the power of processing to quickly deal with vast quantities of data is a must. Technological options, which are illustratively referred to as Apache Hadoop, Spark, and MapReduce, exist to accomplish parallel processing on distributed systems that are still potential enough to carry enormous workloads. The data integration software is an absolute must-have that can gather data from different sources, for example, IoT devices, web applications, and databases. Technologies like Apache Kafka, Apache Flink and others related to real-time analytics provide continuous data streams. Being capable of illustrating data in the optimum way and doing the analytics work, the visualization and analytics software such as Power BI, Tableau, and Python libraries help data move from a raw state to a meaningful state. These technologies are the main elements of a Big Data ecosystem, and they are responsible for bringing about companies' abilities to store, process, analyse and secure data in an efficient manner, and at the same time, nurturing creativity and decision-making at a higher level.

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