Abstract: A computer-implemented method can comprise establishing programmatic connections to a digitally stored first database comprising over one million records, each of the records comprising columns; reading a configuration file that specifies tables in the database; for each particular table, forming and submitting a plurality of queries to the database, each of the queries specifying data aggregation operations, and in response thereto, receiving result sets of records of the database; calculating metadata metrics that characterize columns of the records in the result sets and storing the metadata metrics in tables for string column statistics, numeric column statistics, date column statistics, based upon a particular data type among different data types of the columns; generating presentation instructions which when rendered cause displaying one or more graphical visualizations in a graphical user interface.

Abstract: A computer-implemented method can comprise accessing a trained learning machine, evaluating, using the machine learning model, the transcript to output a first sentiment score related to the first party in the unique domain, accessing digital engagement data representing engagement of the first party with digital assets associated with the second party, evaluating the one or more sentiment score values and the digital engagement data to output a value indicative of a likelihood of the first party to take a particular action, and determining whether the value is above a threshold, and if so, automatically sending a notification to a computer device associated with the second party.

Abstract: A computer-implemented method can comprise establishing programmatic connections to a digitally stored first database comprising over one million records, each of the records comprising time series data organized as an event with a timestamp and an event data, the first database being part of a HADOOP cluster that is programmatically coupled to a HIVE data warehouse manager and a PRESTO query engine, reading a configuration file that specifies one or more tables in the first database and for each particular table among the one or more tables, forming and submitting a plurality of PRESTO queries to the first database, each of the PRESTO queries specifying one or more data aggregation operations, and in response thereto, receiving a set of aggregated records of the first database, training a machine learning model using a portion of the aggregated records as a training dataset, determining a plurality of outlier values that are represented in the set of aggregated records, one or more change points that are rep

Abstract: In one embodiment, a computer system comprises one or more central processing units (CPUs) that are communicatively coupled to a system clock, one or more network interfaces, and one or more database interfaces; digital electronic main memory that is communicatively coupled to the one or more CPUs and storing one or more sequences of stored program instructions which, when executed using the one or more CPUs, cause the one or more CPUs to execute a plurality of different consumer services of a SaaS-based data analytics platform, each of the consumer services hosting an instance of a sandboxed runtime for a hermetic and deterministic programming language; user function storage that is communicatively coupled to one of the database interfaces and storing a plurality of different user functions, each of the user functions having been programmed using the programming language, each of the user functions being stored in association with a reference to a destination table of a destination database; each of the cons

Abstract: A computer-implemented method comprising receiving, from a user computer at a server computer, a configuration file specifying one or more data source identifiers of source database tables in a target database system, one or more data sink identifiers of sink database tables in the target database system, and two or more data transformations; the server computer initiating execution of a data transformation framework by loading one or more configuration parameters of the configuration file into main memory of the server computer to define a workflow; creating and storing a configuration table based on the configuration file, the configuration table comprising a plurality of dynamic queries, a plurality of identifiers of transformation functions, and names of the one or more data transformations; in the configuration table, for each of the transformations, creating a plurality of dynamic common table expression queries, each of the dynamic common table expression queries being associated with a particular tran

Abstract: A computer-implemented method can comprise establishing programmatic connections to a digitally stored first database comprising over one million records, each of the records comprising columns; reading a configuration file that specifies tables in the database; for each particular table, forming and submitting a plurality of queries to the database, each of the queries specifying data aggregation operations, and in response thereto, receiving result sets of records of the database; calculating metadata metrics that characterize columns of the records in the result sets and storing the metadata metrics in tables for string column statistics, numeric column statistics, date column statistics, based upon a particular data type among different data types of the columns; generating presentation instructions which when rendered cause displaying one or more graphical visualizations in a graphical user interface.

Abstract: A system and method for operating a big-data platform that includes at a data analysis platform, receiving discrete client data; storing the client data in a network accessible distributed storage system that includes: storing the client data in a real-time storage system; and merging the client data into a columnar-based distributed archive storage system; receiving a data query request through a query interface; and selectively interfacing with the client data from the real-time storage system and archive storage system according to the query.

Abstract: A system and method for operating a big-data platform that includes at a data analysis platform, receiving discrete client data; storing the client data in a network accessible distributed storage system that includes: storing the client data in a real-time storage system; and merging the client data into a columnar-based distributed archive storage system; receiving a data query request through a query interface; and selectively interfacing with the client data from the real-time storage system and archive storage system according to the query.