Abstract: Data queries that are agnostic to any particular data source may include a data source alias. The data source alias may be replaced with a data source identifier to obtain a data query configured for a target data source. Data processing jobs may be agnostic to any particular data processing platform. A data processing job may include a data processing task that is agnostic to any particular data processing platform. A code library may provide platform-specific code configured to implement a data processing task on a data processing platform. A data query configured for a particular data source and a data processing task configured for a particular data processing platform may be used to create a data processing job. Configurations that restrict execution of a data processing job to execution via an interactive development environment may be removed to allow its execution directly at the data processing platform itself.

Abstract: A microfluidic device for modeling a tumor-immune microenvironment can include a multiwell plate defining a plurality of microenvironment units fluidically coupled with a plurality of wells. Each microenvironment unit of the plurality of microenvironment units can include one or more compartments. Each microenvironment unit can include a trapping feature positioned within the one or more compartments. The trapping feature can be defined by a portion of at least one of a sidewall or a floor of the one or more compartments. The trapping feature can restrict movement of a tissue sample introduced into the one or more compartments and to allow fluid to flow past the tissue sample. The microfluidic device can include a plurality of micropumps each coupled with a respective well and configured to control movement of a respective fluid sample through each respective well.

Abstract: Techniques for performing parallel processing jobs across regionally-distributed servers may include executing a requested job in parallel across two or more regional cloud-based servers, the status of parallel jobs may be locally monitored using respective local instances of a status-tracking database. The multiple instances may replicate modifications of one instance of the status database to other instances of the status database to synchronize the status of regional jobs. Replicating modifications across the respective regional instances of the status database facilitates the parallel executing of the jobs across regions while reducing or avoiding unrecoverable delays if the tasks/job hangs or otherwise fails.

Abstract: Data queries that are agnostic to any particular data source may include a data source alias. The data source alias may be replaced with a data source identifier to obtain a data query configured for a target data source. Data processing jobs may be agnostic to any particular data processing platform. A data processing job may include a data processing task that is agnostic to any particular data processing platform. A code library may provide platform-specific code configured to implement a data processing task on a data processing platform. A data query configured for a particular data source and a data processing task configured for a particular data processing platform may be used to create a data processing job. Configurations that restrict execution of a data processing job to execution via an interactive development environment may be removed to allow its execution directly at the data processing platform itself.

Abstract: Data queries that are agnostic to any particular data source may include a data source alias. The data source alias may be replaced with a data source identifier to obtain a data query configured for a target data source. Data processing jobs may be agnostic to any particular data processing platform. A data processing job may include a data processing task that is agnostic to any particular data processing platform. A code library may provide platform-specific code configured to implement a data processing task on a data processing platform. A data query configured for a particular data source and a data processing task configured for a particular data processing platform may be used to create a data processing job. Configurations that restrict execution of a data processing job to execution via an interactive development environment may be removed to allow its execution directly at the data processing platform itself.

Abstract: Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.

Abstract: Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.

Abstract: Data queries that are agnostic to any particular data source may include a data source alias. The data source alias may be replaced with a data source identifier to obtain a data query configured for a target data source. Data processing jobs may be agnostic to any particular data processing platform. A data processing job may include a data processing task that is agnostic to any particular data processing platform. A code library may provide platform-specific code configured to implement a data processing task on a data processing platform. A data query configured for a particular data source and a data processing task configured for a particular data processing platform may be used to create a data processing job. Configurations that restrict execution of a data processing job to execution via an interactive development environment may be removed to allow its execution directly at the data processing platform itself.

Abstract: Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.

Abstract: Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.

Abstract: A microfluidic device for modeling a tumor-immune microenvironment can include a multiwell plate defining a plurality of microenvironment units fluidically coupled with a plurality of wells. Each microenvironment unit of the plurality of microenvironment units can include one or more compartments. Each microenvironment unit can include a trapping feature positioned within the one or more compartments. The trapping feature can be defined by a portion of at least one of a sidewall or a floor of the one or more compartments. The trapping feature can restrict movement of a tissue sample introduced into the one or more compartments and to allow fluid to flow past the tissue sample. The microfluidic device can include a plurality of micropumps each coupled with a respective well and configured to control movement of a respective fluid sample through each respective well.

Abstract: Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.

Abstract: Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.