The age of artificial intelligence (AI) has begun, and society is having to quickly adapt at all levels in order to keep up with the technology. We are seeing the integration of AI across industries, and in each industry, there is predicted to be a massive investment in AI technology.
An example of how quickly AI is progressing is the facial recognition industry. Currently, the industry is worth $8.83 billion, but as AI becomes more widely integrated into the industry, the industry will be worth $24.28 billion by 2032.
Finance, healthcare, and manufacturing have also undergone a significant shift toward AI technology, which will completely redefine society. While much of the focus around AI has been on its applications, less attention has been given to the data that powers and supports it. Every type of AI requires vast amounts of data to train the applications and provide accurate services. Such is the need for new data that AI data demand is outpacing supply, even though more data is now produced every month than existed in total just over a decade ago. In order to store the data to be used by AI, industries are turning to advanced data management systems, with NoSQL databases being a top choice due to their AI capabilities.
NoSQL Databases
Unlike traditional relational databases that store data in columns and tables, NoSQL databases come in a variety of types based on their data model. The four main types are document databases, key-value databases, wide-column stores, and graph databases, each of which provides flexible schemas and scales easily with large amounts of big data and high user loads. Each NoSQL database offers different features that can be tailored to the needs of the user. For example, graph databases are more suited for analyzing complex relationships and patterns between entities. In contrast, document databases provide a more flexible, natural way of storing and retrieving large data volumes of similar types as documents. One ability that links all the NoSQL databases is that they can be used for AI.
Why are NoSQL Databases Used for AI?
NoSQL Databases Can Handle Unstructured Data
AI applications use every type of data to either train on or perform a service. Much of this data is unstructured or semi-structured, such as text documents, images, audio files, and videos. Unlike relational databases, which are primarily designed to store structured data, NoSQL databases can store unstructured data. Due to the different database formats, NoSQL databases don’t have a rigid schema, which allows them to store both established data types and new ones. With AI constantly evolving, NoSQL databases are able to keep up with the evolving data demands.
High Performance
AI applications are very resource-heavy due to the intense computational demands of training and running AI models. NoSQL databases excel in handling large volumes of data and high-velocity streams, making them suitable for real-time AI applications. A key reason for this is that they are optimized for fast read and write speeds, allowing them to keep up with the requirements of AI. Most NoSQL databases are distributed, which allows them to quickly spread data across different servers to prevent any overload issues. AI datasets can range from thousands to billions of entries, which is why a database that can handle this workload without a dip in performance is vital.
Data Analytics
The ability to analyse and use data is a key component of AI applications and is how they are able to provide accurate and personalized results. NoSQL database improves data velocity, which is the speed and frequency of data generation and processing that data analytics and AI applications need to cope with. As a result, AI is able to use the data on NoSQL databases to extract insights, make predictions, and guide decision-making. Generative AI applications use data analytics capabilities to train large language models (LLMs) such as chatbots and content creation applications.
As the world becomes more integrated with AI, databases that can handle the huge demands of these applications will be required. Due to their flexibility, high performance, and data analytics capabilities, NoSQL databases are at the forefront of the evolution of AI. For more AI articles, make sure to visit our dedicated section.
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