The term “big data” has come to define the way that we see data in the modern digital world. At the simplest level, we associate it with large social media platforms with a global user base that need to store, retrieve, and analyze multiple data sets quickly in real time. In this context, big data is really a component of developments in AI and deep machine learning where the capacity to access information across distributed networks is crucial.
What Is an Analytic Database?
Many of us are familiar with the concept of database software from our earlier school years where we might have created, stored, and retrieved very simple records. These days, analytic database software is used by commercial entities and public organizations to store so-called “big data” and allow users to analyze and interact with it easily and at great speed.
An analytical data store is typically a series of related data clusters distributed across multiple servers where data is stored. The fastest analytical data store implementations often feature an in-memory database for increased storage and retrieval speed, rather than cold storage.
Where Big Data and Analytic Database Software Is Used
Here are just several examples of where the speed of an analytical data store and cross-platform database might be used:
- Social Media: These platforms need to handle huge amounts of user-generated information in real time. In fact, we have already crossed the threshold in which AI and deep learning is being used on a regular basis to manage the user experience; this is where the capacity to manage and manipulate big data is essential.
- Financial: The financial markets are all about managing and manipulating data and this is where being able to access information at great speed and analyze it in real time is crucial.
- Scientific: Both public and private scientific organizations require ongoing access to big data in real time for the purposes of scientific analysis.
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