Buried low in the software stack of most applications is a data engine, an embedded key-value store that sorts and indexes data. Until now, data engines—sometimes called storage engines—have received little focus, doing their thing behind the scenes, beneath the application and above the storage.
A data engine usually handles basic operations of storage management, most notably to create, read, update, and delete (CRUD) data. In addition, the data engine needs to efficiently provide an interface for sequential reads of data and atomic updates of several keys at the same time.
Organizations are increasingly leveraging data engines to execute different on-the-fly activities, on live data, while in transit. In this kind of implementation, popular data engines such as RocksDB are playing an increasingly important role in managing metadata-intensive workloads, and preventing metadata access bottlenecks that may impact the performance of the entire system.
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