Abstract: Extract, Transform, Load (ETL) processing may be initiated by detected events. A trigger event may be associated with an ETL process apply one or more transformations to a source data object. The trigger event may be detected for the ETL process and evaluated with respect to one or more execution conditions for the ETL process. If the execution conditions for the ETL process are satisfied, then the ETL process may be executed. At least some of the source data object may be obtained, the one or more transformations of the ETL process may be applied, and one or more transformed data objects may be stored.

Abstract: Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Abstract: Recognizing unknown data objects may be implemented for data objects stored in a data store. Data objects that are identified as unknown may be accessed to retrieve a portion of the data object. Different representations of the data object may be generated for recognizing different data schemas. An analysis of the representations may be performed to identify a data schema for the unknown data object. The data schema may be stored in a metadata store for the unknown data object.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

Abstract: Data transformation workflows may be generated to transform data objects. A source data schema for a data object and a target data format or target data schema for a data object may be identified. A comparison of the source data schema and the target data format or schema may be made to determine what transformations can be performed to transform the data object into the target data format or schema. Code to execute the transformation operations may then be generated. The code may be stored for subsequent modification or execution.

Abstract: Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

Abstract: Extract, Transform, Load (ETL) processing may be initiated by detected events. A trigger event may be associated with an ETL process apply one or more transformations to a source data object. The trigger event may be detected for the ETL process and evaluated with respect to one or more execution conditions for the ETL process. If the execution conditions for the ETL process are satisfied, then the ETL process may be executed. At least some of the source data object may be obtained, the one or more transformations of the ETL process may be applied, and one or more transformed data objects may be stored.

Abstract: Extract, Transform, Load (ETL) processing may be initiated by detected events. A trigger event may be associated with an ETL process apply one or more transformations to a source data object. The trigger event may be detected for the ETL process and evaluated with respect to one or more execution conditions for the ETL process. If the execution conditions for the ETL process are satisfied, then the ETL process may be executed. At least some of the source data object may be obtained, the one or more transformations of the ETL process may be applied, and one or more transformed data objects may be stored.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

Abstract: History for data objects may be maintained to detect data events. An indication of an Extract, Transform, Load (ETL) process applied to one or more source data objects to generate one or more transformed data objects may be received. History for the source data objects may be updated to include the transformed data objects and the ETL process that generated the transformed data objects. An evaluation of the update may be performed to determine whether an event associated with the data lineage is triggered. If the event is triggered, a notification of the event may be sent to one or more subscribers for the event.

Abstract: Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Abstract: Data transformation workflows may be generated to transform data objects. A source data schema for a data object and a target data format or target data schema for a data object may be identified. A comparison of the source data schema and the target data format or schema may be made to determine what transformations can be performed to transform the data object into the target data format or schema. Code to execute the transformation operations may then be generated. The code may be stored for subsequent modification or execution.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

Abstract: Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Abstract: Data transformation workflows may be generated to transform data objects. A source data schema for a data object and a target data format or target data schema for a data object may be identified. A comparison of the source data schema and the target data format or schema may be made to determine what transformations can be performed to transform the data object into the target data format or schema. Code to execute the transformation operations may then be generated. The code may be stored for subsequent modification or execution.

Abstract: Isolation types may be determined for resources that execute portions of code. Code may be received via a network-based interface from a client for execution. An execution plan for the code may be generated and evaluated to determine one or more isolation types for computing resources that execute the code. The computing resources that are configured to provide the determined isolation types may then be identified and execution of the code initiated at the identified computing resources.

Abstract: Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

Abstract: Recognizing unknown data objects may be implemented for data objects stored in a data store. Data objects that are identified as unknown may be accessed to retrieve a portion of the data object. Different representations of the data object may be generated for recognizing different data schemas. An analysis of the representations may be performed to identify a data schema for the unknown data object. The data schema may be stored in a metadata store for the unknown data object.

Abstract: History for data objects may be maintained to detect data events. An indication of an Extract, Transform, Load (ETL) process applied to one or more source data objects to generate one or more transformed data objects may be received. History for the source data objects may be updated to include the transformed data objects and the ETL process that generated the transformed data objects. An evaluation of the update may be performed to determine whether an event associated with the data lineage is triggered. If the event is triggered, a notification of the event may be sent to one or more subscribers for the event.