Amazon Technologies, Inc.

Gopinath Duddi

Anurag Windlass Gupta

Mehul A Shah

George Steven McPherson

Prajakta Datta Damle

Akshat Vig

Rama Krishna Sandeep Pokkunuri

Rajaprabhu Thiruchi Loganathan

Parikshit Shivajirao Pol

Subramanian Sankara Subramanian

Safeer Mohiuddin

Supratik Chakraborty

Sudarshan Narasimhan

Benjamin Albert Sowell

Bohou Li

Amit Gul Phagwani

Romi Boimer

Ronald Stephen Kyker

Surbhi Dangi

Sandhya Edupuganti

Rama Krishna Pokkunuri

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

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

Information describing changes to a collection of items maintained by a database may be stored in a log file. The information in the log file may be converted into a stream of records describing the changes. The records may be directed to a computing node selected for performing a trigger function in response to the change, based on applying a hash function to a portion of the record, identifying a hash space associated with a value output by the hash function, and mapping from the hash space to the selected computing node.

Distributed stream-based database triggers

Techniques for visual data preparation are described. An interactive visual data preparation service provides a user with a graphical user interface that presents values from a sample taken of a dataset along with statistical information associated with those values. A user uses the graphical user interface to test out various transformations to the sample dataset by applying transformations and viewing near-immediate results of those transformations as applied to the sample. The desired set of transformations is represented as a recipe object, which can be used to perform data preparation against the overall dataset or other datasets on behalf of the user or other users.

Interactive visual data preparation service

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.

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San Jose, CA, United States of America

Gopinath Duddi