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 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 event data, the first database being a multi-tenant data store programmatically coupled to a warehouse manager and a 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 queries to the first database to receive a set of aggregated records of the first database, training a machine learning model using a portion of the aggregated records, receiving, from a second computer, input specifying a plurality of hyper-parameters for the machine learning model, evaluating the aggregated records of the first database using the machine learning model to output forecasted event data.

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: 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 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 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: 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.