The rapid growth of data-intensive use cases such as simulations, streaming applications (like IoT and sensor feeds), and unstructured data has elevated the importance of performing fast database operations such as writing and reading data—especially when those applications begin to scale. Almost any component in a system can potentially become a bottleneck, from the storage and network layers through the CPU to the application GUI.
As we discussed in “Optimizing metadata performance for web-scale applications,” one of the main reasons for data bottlenecks is the way data operations are handled by the data engine, also called the storage engine—the deepest part of the software stack that sorts and indexes data. Data engines were originally created to store metadata, the critical “data about the data” that companies utilize for recommending movies to watch or products to buy. This metadata also tells us when the data was created, where exactly it’s stored, and much more.
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