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A large language model is built upon a deep learning architecture known as a transformer neural network, which is designed to process and generate human language. It’s “large” because it contains numerous parameters (weights and connections) that allow it to learn intricate patterns and relationships in language data.
Businesses require a strong infrastructure in order to truly harness the potential of this data. In recent times, Data Mesh and Data Fabric have emerged as reliable solutions to effectively manage large volumes of data.
In the era of Big Data where organizations are facing the challenge of handling large volumes of diverse data, traditional data management approaches, such as data warehouses and data lakes are simply not enough. A new paradigm has emerged in this space that combines the best of both worlds – Data Lakehouse.
BigQuery is a serverless and enterprise data warehouse offered by Google Cloud. It enables organizations to focus on analytics and productivity by relieving them from the burden of infrastructure management. While BigQuery provides powerful features out of the box, optimizing the costs associated with storing and processing data is essential.
Modern organizations encounter data-related challenges such as privacy concerns and limited data diversity, which can significantly impede their ability to develop effective decision-making. Read in detail to know more how Synthetic Data Safeguarding Data Confidentiality.