Abstract: Methods and systems for dynamically scheduling data processing are disclosed. An example method includes: identifying a data model to be built, the data model being associated with a data model definition defining input data to be used in building that data model; determining a size of the input data; obtaining an expected access time for the data model; estimating a total time required for building the data model based on the size of the input data and the definition of the data model; determining a time to start building the data model based on the expected access time for the data model and the estimated total time required to build the data model; and scheduling the building of the data model to start at the determined time.

Abstract: Data may be queried and analyzed in order to draw insights. One type of data query that may be performed is a funnel query. A funnel query is a query characterized by a sequence of events, e.g.: “In the last N days, how many unique users performed event A, then event B, and then event C”. Systems and methods for performing funnel queries are provided herein. In some embodiments, the speed at which a computer can answer a funnel query may be increased. In some embodiments, a bitmap is used to eliminate one or more sequences of events that would otherwise need to be traversed during the funnel query. In some embodiments, a sequence of events is stored across multiple data partitions, each data partition covering a different period of time.

Abstract: Data may be queried and analyzed in order to draw insights. One type of data query that may be performed is a funnel query. A funnel query is a query characterized by a sequence of events, e.g.: “In the last N days, how many unique users performed event A, then event B, and then event C”. Systems and methods for performing funnel queries are provided herein. In some embodiments, the speed at which a computer can answer a funnel query may be increased. In some embodiments, a bitmap is used to eliminate one or more sequences of events that would otherwise need to be traversed during the funnel query. In some embodiments, a sequence of events is stored across multiple data partitions, each data partition covering a different period of time.

Abstract: Methods and systems for dynamically scheduling data processing are disclosed. An example method includes: identifying a data model to be built, the data model being associated with a data model definition defining input data to be used in building that data model; determining a size of the input data; obtaining an expected access time for the data model; estimating a total time required for building the data model based on the size of the input data and the definition of the data model; determining a time to start building the data model based on the expected access time for the data model and the estimated total time required to build the data model; and scheduling the building of the data model to start at the determined time.

Abstract: Data may be queried and analyzed in order to draw insights. One type of data query that may be performed is a funnel query. A funnel query is a query characterized by a sequence of events, e.g.: “In the last N days, how many unique users performed event A, then event B, and then event C”. Systems and methods for performing funnel queries are provided herein. In some embodiments, the speed at which a computer can answer a funnel query may be increased. In some embodiments, a bitmap is used to eliminate one or more sequences of events that would otherwise need to be traversed during the funnel query. In some embodiments, a sequence of events is stored across multiple data partitions, each data partition covering a different period of time.

Abstract: Data may be queried and analyzed in order to draw insights. One type of data query that may be performed is a funnel query. A funnel query is a query characterized by a sequence of events, e.g.: “In the last N days, how many unique users performed event A, then event B, and then event C”. Systems and methods for performing funnel queries are provided herein. In some embodiments, the speed at which a computer can answer a funnel query may be increased. In some embodiments, a bitmap is used to eliminate one or more sequences of events that would otherwise need to be traversed during the funnel query. In some embodiments, a sequence of events is stored across multiple data partitions, each data partition covering a different period of time.

Abstract: Methods and apparatus for configurable-capacity time-series tables are disclosed. A schedule of database table management operations, including at least an operation to change a throughput constraint associated with a table in response to a triggering event, is generated. The table is instantiated with an initial throughput constraint in accordance with the schedule. Work requests directed to the table are accepted based on the initial throughput constraint. The throughput constraint is modified in response to the triggering event. Subsequent work requests are accepted based on the modified throughput constraint.

Abstract: Methods and apparatus for configurable-capacity time-series tables are disclosed. A schedule of database table management operations, including at least an operation to change a throughput constraint associated with a table in response to a triggering event, is generated. The table is instantiated with an initial throughput constraint in accordance with the schedule. Work requests directed to the table are accepted based on the initial throughput constraint. The throughput constraint is modified in response to the triggering event. Subsequent work requests are accepted based on the modified throughput constraint.

Abstract: Methods and apparatus for configurable-capacity time-series tables are disclosed. A schedule of database table management operations, including at least an operation to change a throughput constraint associated with a table in response to a triggering event, is generated. The table is instantiated with an initial throughput constraint in accordance with the schedule. Work requests directed to the table are accepted based on the initial throughput constraint. The throughput constraint is modified in response to the triggering event. Subsequent work requests are accepted based on the modified throughput constraint.