A few days back, Microsoft revealed new capabilities for Azure Archive Storage including priority retrieval, an updated Copy Blob API, and more, in general availability. Earlier today, the tech giant also announced that Azure Government would now include the Azure Migrate service as well.
Now, in more cloud-related news, a new capability dubbed Query Acceleration has been brought to Microsoft's Azure Data Lake Storage (ADLS) offerings. This feature is currently available in the form of a public preview.
With the release of Query Acceleration, the Redmond firm believes that it has provided Data Lake customers with the opportunity to bypass the trade-off between performance and cost, achieving a boost in both departments. This is done through the application of specific predicates and column projections to filter data, allowing clients to receive streamed responses. In essence, this nullifies the need to filter and process unrequired data, thereby reducing cost and improving performance.
The way in which a typical application may utilize Query Acceleration has been described in detail below through the diagram and step-by-step working:
- The client application requests file data by specifying predicates and column projections.
- Query Acceleration parses the specified query and distributes work to parse and filter data.
- Processors read the data from the disk, parses the data by using the appropriate format, and then filters data by applying the specified predicates and column projections.
- Query Acceleration combines the response shards to stream back to client application.
- The client application receives and parses the streamed response. The application doesn't need to filter any additional data and can apply the desired calculation or transformation directly.
Interested users can sign up for the public preview of ADLS Query Acceleration. To learn more about how the capability works with Java and .NET, you can check out its documentation here.