Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
AI-Assisted Analytics is an advanced analysis method performed through the use of artificial intelligence and machine learning algorithms in data analysis processes. This technology has the ability to detect complex relationships in large datasets, identify patterns, and make predictions for the future.
Data Lakehouse is a modern data management approach that combines the advantages of data warehouse and data lake architectures. This structure offers the ability to process both structured and unstructured data on a single platform, making data analytics and big data processing processes more efficient.
Amazon Bedrock is a platform offered by Amazon Web Services (AWS) and designed for companies looking to develop generative AI applications
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