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Data fragmentation is a process of dividing large data sets into smaller and more manageable parts. Each smaller piece of data is known as a fragment or shard, and these smaller parts allow for efficient storage and retrieval of large amounts of data. Fragmentation can be implemented at different levels, such as the database, file system, or application layer.
Data fragmentation divides the large data set into smaller fragments, or shards, each representing a specific subset of the data. The data can be partitioned based on different criteria, such as time, geography, or user profiles. As a result, when a query is issued, it only needs to access the relevant shards, rather than the entire dataset, which leads to faster processing times and lower latency.
Data fragmentation can bring many benefits to businesses. By dividing data into smaller, more manageable parts, it enables efficient processing and analysis of large datasets. This allows businesses to respond quickly to changing demands and identify key insights that might have been hidden in larger datasets. It also reduces costs for storing and processing large data sets, making it an ideal solution for businesses looking to optimize their data management processes.
Some of the most important use cases of data fragmentation include:
Other closely related terms and technologies include:
Data fragmentation is a key feature of Dremio, allowing users to divide their data into smaller, more manageable parts, improving processing times and enabling faster analysis. With Dremio, businesses can achieve greater insights into their data, improve performance and scalability, and reduce costs.