Building Cognitive Data Lakes on Cloud: Integrating NLP and AI to Make Data Lakes Smart
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The enormous increase in the volume of digital data in all industries has made organizations look for more efficient storage and processing techniques for data which has provided further impetus for the change from conventional data lakes to cognitive data lakes. In addition to being a structured or unstructured data pool, cognitive data lakes have AI and NLP strategic built-in features to offer real-time intelligent data analytics to support the organization’s strategic decisions and plans (Smith et al., 2023). Consequently, they provide a more effective method for data utilization enabling enterprises to get context, sentiment and value from elaborate data. These data lakes can grow on demand by procuring additional cloud infrastructure which fulfils the requirements of large data storage and computing while containing costs (Johnson & Lee, 2022).
This article extends the discussion of CDLs and CI on cloud infrastructures to discuss the architectural and technical considerations for cognitive data lakes that include information and natural language models for contextualizing and classifying data. In this paper, we consider the detailed usage of NLP, which is applied to converting the best textual data into structured insights using such approaches as entity extraction or sentiment analysis as well as topic modeling, which is also useful in understanding how textual data can be used effectively in practice by organizations (Brown, 2024). In addition, we measure the effects of machine learning algorithms in sorting, sifting, and forecasting data patterns in such lakes, building an interactive and cognitive data environment (Garcia & Patel, 2023).
Nevertheless, cognitive data lakes are not problem-free solutions and certain challenges are worth discussing. Some of the problems that organisations have to solve include quality of the data, its security, and compliance particularly if the information shared is sensitive and takes place in distributed structures (Davis, 2024). In this paper, we cover detailed information about the strategies and approaches that should be used in order to overcome the given challenges, including data governance strategies, cloud-native security practices, and more. In specific case descriptions, we demonstrate how cognitive data lakes work in practice across industries like healthcare, finance, and retail with tangible examples related to productivity, customer satisfaction, and market differentiation (Xu, 2023).
Thus, we conclude with a discussion on future prospects of cognitive data lakes by taking into account the innovative solutions of AI and NLP to advance the intuitiveness of cognitive data lake in the future. With newer trends arising in the future including generative AI, real-time analytics, advanced NLP methods, cognitive data lakes can therefore be expected to become more essential in helping adopters derive valuable predictions and responses to changes in the market (Chen & Li, 2024). In essence, this article offers a futuristic view of cognitive data lakes with emphasis on their chief positionality in the operating data environment.
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Copyright (c) 2024 Kiran Randhi, Srinivas Reddy Bandarapu
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