Amazon Technologies, Inc.

George Steven McPherson

Anurag Windlass Gupta

Mehul A Shah

Prajakta Datta Damle

Gopinath Duddi

Supratik Chakraborty

Benjamin Albert Sowell

Xing Wu

Andrew Edward Caldwell

Peter Sirota

Richard Jeffrey Cole

Yevgeniy Mikhaylyuta

Jonathan Andrew Fritz

Bohou Li

Ankit Kamboj

Tobin Daniel Baker

Robert Frederick Leidle

Abhishek Rajnikant Sinha

Sumit Kumar

Jian Fang

Mathew Mills

Bogdan Eduard Ghidireac

Xiqiang Zhi

Sandhya Edupuganti

Vageesh Kumar

Dag Stockstad

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.

Dynamically routing code for executing

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.

Event driven extract, transform, load (ETL) processing

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.

Recognizing unknown data objects

A metadata catalog may store metadata about data sources. The metadata may define table, column, partition and/or data formats, as well as title information, textual descriptions, modification information, data schemas and access information, such as location and connection information. A metadata catalog service may provide a shared, persistent, central repository for the metadata. The metadata of the catalog may be published via multiple access mechanisms and programmatically obtained and processed by other programs via the metadata catalog service. Metadata consumers may utilize the metadata catalog service to perform various metadata tasks, such as metadata retrieval, creation, addition, modification and/or deletion. The metadata catalog service may be directly integrated into existing data analytic tools as a persistent and durable metadata store allowing users to utilize the broad set of data processing tools.

Persistent metadata catalog

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.

Generating data transformation workflows

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.

Dynamic generation of data catalogs for accessing data

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.

Maintaining data lineage to detect data events

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.

Determining isolation types for executing code portions

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.

Hosting version controlled extract, transform, load (ETL) code

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Seattle, WA, United States of America

George Steven McPherson