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: A data transformation system includes a schema inference module and an export module. The schema inference module is configured to dynamically create a cumulative schema for objects retrieved from a first data source. Each of the retrieved objects includes (i) data and (ii) metadata describing the data. Dynamically creating the cumulative schema includes, for each object of the retrieved objects, (i) inferring a schema from the object and (ii) selectively updating the cumulative schema to describe the object according to the inferred schema. The export module is configured to output the data of the retrieved objects to a data destination system according to the cumulative schema.

Abstract: Version controlled Extract, Transform, Load (ETL) code may be hosted for developing or executing the ETL job in an ETL system. A version of ETL code may be obtained from version controlled code store and maintained in a data store. Development or execution clients may submit access requests for the version of ETL code which may be serviced from the version stored in the data store. Updates to the version of the ETL code may be eventually committed to the version controlled code store. The latest version of ETL code may also be obtained from the version controlled code store when providing the ETL code in response to a request to retrieve the ETL code.

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: A transactional memory system uses a volatile memory as primary storage for transactions. Data is selectively stored in a non-volatile memory to impart durability to the transactional memory system to allow the transactional memory system to be restored to a consistent state in the event of data loss to the volatile memory.

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.

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.

Abstract: A first column of a first database table may be transferred to a second table in a combined truncate and append operation in a transaction that may be atomic and reversible. References data for the first column may be removed from the first table and added to the second table. The combined operation may be reversed by removing, from the second table, references to blocks of data added to the second table prior to commencing the combined operation. A second column corresponding on a row-by-row basis to the first column may be added to the second table, where the data is indicative of visibility of data in the first column to other transactions on the second table.

Abstract: A method of operating a data analysis system includes retrieving objects from a data source. Each of the retrieved objects includes (i) data and (ii) metadata describing the data. The method further includes dynamically creating a cumulative schema by, for each object of the retrieved objects: (i) inferring a schema from the object based on the metadata of the object and inferred data types of elements of the data of the object, (ii) creating a unified schema, wherein the unified schema describes both (a) the object described by the inferred schema and (b) a cumulative set of objects described by the cumulative schema, and (iii) storing the unified schema as the cumulative schema. The method further includes exporting the data of each of the retrieved objects to a data warehouse.

Abstract: A method of operating a query system includes retrieving objects from a data source. Each of the retrieved objects includes (i) data and (ii) metadata describing the data. The method further includes dynamically creating a cumulative schema. The dynamically creating includes, for each object of the retrieved objects, (i) inferring a schema from the object based on the metadata of the object and inferred data types of elements of the data of the object, (ii) creating a unified schema, and (iii) storing the unified schema as the cumulative schema. The unified schema describes both (a) the object described by the inferred schema and (b) a cumulative set of objects described by the cumulative schema. The method further includes storing the data of each of the retrieved objects in a storage service.

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: 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: 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.