Atscale, Inc.

David P Mariani

Sarah Gerweck

Matthew Baird

David Vigdor Schreibman

Josh Klahr

David A Ross

Techniques are presented for performing enhanced real-time data statement monitoring and control without requiring modifications to data analysis client applications or data storage environments. A data statement processing layer is implemented between the data analysis client applications and the data storage environments. A data statement management agent interacts with an event bus in the data statement processing layer. The data statement management agent accesses event objects on the event bus that are generated during the processing of data statements. Rules are applied to select particular event objects. The selected event objects are transformed into event messages that are delivered to the client applications to monitor the processing of the data statements. Control commands received from the client application are transformed into event objects that are issued to the event bus to control the processing of the data statement.

Data statement monitoring and control

Techniques are presented for rule-based selection of query engines for performing data operations on a variety of datasets stored in heterogeneous data storage environments. Data statements for a subject dataset are mapped to a logical data structure representation of the subject dataset to formulate a logical plan for the data statements. The data statements are analyzed to derive a set of statement attributes associated with the data statements. Query engine attributes describing the query engines available to query various instances of the subject dataset are accessed. A set of query engine selection rules are evaluated subject to the statement attributes and/or the query engine attributes to dynamically select a target query engine for the data statements. A physical plan structured for the target query engine is generated from the logical plan. The physical plan is executed on the target query engine to perform data operations on the subject dataset.

Query engine selection

The present disclosure provides improved systems, methods, and computer program products suited to address the issues with legacy approaches. Specifically, the present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for interpreting relational database statements using a virtual multidimensional data model. Certain embodiments are directed to technological solutions for receiving a relational database statement comprising relational metadata mapped to a virtual multidimensional representation of a subject database, and generating a subject database statement based at least in part on the relational database statement and the virtual multidimensional representation, and configurable to operate on the subject database to produce a visualization result set, which embodiments advance the technical fields for addressing the problem of projecting a multidimensional data view of a subject database on to a relational data analysis environment to enable real time data analyses, as well as advancing peripheral technical fields.

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Santa Clara, CA, United States of America

David P Mariani