Abstract: Techniques for processing data streaming from one or more event sources in order to recreate a database table with database transactions for real-time analytics. An exemplary technique includes receiving a stream of events comprising change data for a table attribute, populating an in-memory table with the change data based on a token associated with an operation type and a mapping of a first attribute and a second attribute to a key attribute of the in-memory table, executing a continuous query on the change data for the key attribute of the in-memory table to generate events comprising data for a third attribute, and outputting the events having an output shape comprising the third attribute, where the events include the data for the third attribute.

Abstract: Techniques for processing data streaming from one or more event sources in order to recreate a database table with database transactions for real-time analytics. An exemplary technique includes receiving a stream of events comprising change data for a table attribute, populating an in-memory table with the change data based on a token associated with an operation type and a mapping of a first attribute and a second attribute to a key attribute of the in-memory table, executing a continuous query on the change data for the key attribute of the in-memory table to generate events comprising data for a third attribute, and outputting the events having an output shape comprising the third attribute, where the events include the data for the third attribute.

Abstract: Techniques for processing data streaming from one or more event sources in order to recreate a database table with database transactions for real-time analytics. An exemplary technique includes receiving a stream of events comprising change data for a table attribute, populating an in-memory table with the change data based on a token associated with an operation type and a mapping of a first attribute and a second attribute to a key attribute of the in-memory table, executing a continuous query on the change data for the key attribute of the in-memory table to generate events comprising data for a third attribute, and outputting the events having an output shape comprising the third attribute, where the events include the data for the third attribute.

Abstract: Techniques for implementing logical queries in a distributed stream processing system using automatic branching and joins. An exemplary technique includes determining a query is a logical query. The logical query includes two or more summaries based on different groups configured to execute in a single query stage of a stream analytics application. The technique further includes converting the logical query into one or more physical queries. The one or more physical queries are separated into individual query stages, and each of the query stages includes a summary from the two or more summaries that is based on an associated group. The technique further includes generating a directed acyclic graph for the one or more physical queries. The directed acyclic graph includes a physical query transformation for each of the individual query stages.

Abstract: Techniques for implementing logical queries in a distributed stream processing system using automatic branching and joins. An exemplary technique includes determining a query is a logical query. The logical query includes two or more summaries based on different groups configured to execute in a single query stage of a stream analytics application. The technique further includes converting the logical query into one or more physical queries. The one or more physical queries are separated into individual query stages, and each of the query stages includes a summary from the two or more summaries that is based on an associated group. The technique further includes generating a directed acyclic graph for the one or more physical queries. The directed acyclic graph includes a physical query transformation for each of the individual query stages.

Abstract: Techniques for processing data streaming from one or more event sources in order to recreate a database table with database transactions for real-time analytics. An exemplary technique includes receiving a stream of events comprising change data for a table attribute, populating an in-memory table with the change data based on a token associated with an operation type and a mapping of a first attribute and a second attribute to a key attribute of the in-memory table, executing a continuous query on the change data for the key attribute of the in-memory table to generate events comprising data for a third attribute, and outputting the events having an output shape comprising the third attribute, where the events include the data for the third attribute.