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New data tools you should know

Big Data” is one of the most used terms in technology. With the volume of data created every minute by customers and organizations around the world, Big Data Analytics has a lot of promise. Hence, large amounts of useful data generated by businesses need to be managed, stored, visualized, and analyzed.

Since traditional data tools aren’t built to handle this level of complexity and volume, a plethora of specialized Big Data software tools and architectural solutions have emerged to handle this workload.

Businesses can use custom-built Big Data Tools to put their data to work, find new opportunities, and establish new business models. In this blog, you will understand the need for Big Data Tools and Technologies. In addition, you will learn the important factors to keep in mind while selecting the right Big Data Tool for your organization.

Big Data refers to vast, diversified amounts of data that are growing at an exponential rate. It is described as “Big” not just because of its size, but also because of its enormous diversity and complexity. Its’ capacity to acquire, organize, and process it typically exceeds that of traditional databases.

Big Data can also come from anywhere on the planet that we can monitor digitally. While there are several definitions for Big Data, the majority of them involve the notion of the “5 V’sof Big Data:

  • Volume: It is important to consider the amount of data available. You’ll have to analyze a lot of low-density, unstructured data. The volume of Big Data can be tens of gigabytes of data for certain businesses or hundreds of petabytes for others.

  • Velocity: The pace at which data is received and acted on is referred to as velocity. In most cases, data is streamed directly into memory rather than being written to the disc. Some internet-connected smart devices function in real-time or near-real-time, necessitating real-time evaluation and response.

  • Variety: The different types of data that are available are referred to as variety. Traditional data formats were well-structured and fit into a relational database with ease. With the growth of Big Data, new unstructured data kinds have emerged. To infer meaning and support metadata, unstructured and semi-structured data formats like text, audio, and video require further preprocessing.

  • Veracity: The models constructed on the data will not be of actual value without this attribute, given the volume, diversity, and pace that Big Data provides. The credibility of the original data, as well as the quality of the data generated after processing, is referred to as veracity. Data biases, abnormalities or inconsistencies, volatility, and duplication, among other factors, should be mitigated by the system.

  • Value: Value is the most essential V in the business world. An enormous volume of information is produced daily, but collecting data is not the mere solution for businesses. Organizations invest in several Big Data technologies, as it not only facilitates Data Aggregation and Storage but also assists in garnering insights from raw data that could help companies gain a competitive edge in the market.

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