Amazon Technologies, Inc.

University of California

Bioheatmap, Inc.

Databricks Inc.

Yannis Papakonstantinou

Vuk Ercegovac

Tate Andrew Certain

Omer Ahmed Zaki

Rohan Deshpande

Karthik Gurumoorthy Subramanya Bharathy

Rajib Dugar

Shivateja Reddy Ummenthala

Allan Henry Vermeulen

Christopher Richard Jacques De Kadt

Gaurav Saxena

Balakrishnan Narayanaswamy

Enrico Siragusa

Andre Hernich

Ippokratis Pandis

Kian Win Ong

Gokul Soundararajan

Andrey Balmin

Sriram Krishnamurthy

George Constantin Caragea

Evangelos Christidis

Panagiotis Parchas

Keliang Zhao

Nikolaos Koulouris

Jiayuan Chen

Robert Meadows

Michail Petropoulos

Michael Paul Armbrust

Christos Faloutsos

Jonathan Andrew Fritz

Fabian Oliver Nagel

Mukul Murthy

Nitin Sharma

Mario Guerriero

Paul Lappas

Xi Liang

Joseph Torres

John Sismanis

Min Yang

Yupeng Fu

Ioannis Katsis

A database management system receives a request to perform a transaction. The database management system commits the transaction, and in response to committing the transaction, generates a cryptographic hash based on an attribute of the transaction. The cryptographic hash is stored in a leaf-region of a hash tree. In response to a request to verify the transaction, signatures are retrieved from the tree based on a traversal of the tree to locate the node corresponding to the transaction. The retrieved signatures are used to verify the transaction.

Cryptographic verification of database transactions

A system receives ETL specification for processing stream data, including a transform operation represented using a database query specification. The system generates a dataflow graph of a sequence of database queries by decomposing the database query into a first database query that generates an intermediate results table, and a second database query that receives as input the intermediate results table and outputs data used for performing the transform operation. The system executes the sequence of database queries for performing the transform operation on stream data received from the source. When receiving an incremental data set, the system determines an output change set based on the received incremental data set by traversing an execution plan and processing each operator in the execution plan, and computing a change set of a particular operator from the change sets output by the one or more other operators based on the incremental data set.

Incremental execution of extract, transform, load process using microtechniques architecture

A new type system may be added to a type registry for a data processing service. A request to add the new type system may be received that describes the new type system for a data store. The new type system may be used to perform a data processing job that accesses the data store to obtain or store data as a source or target data store.

Registering additional type systems using a hub data model for data processing

Materialized views may be generated at a managed materialized view platform that accepts data streams as a source. When updating a materialized view with a data stream as a source, a stored portion of the stream is used to maintain a history of changes to be made. When a join operation needs to utilize the data stream source to determine an update to the materialized view, the data stream is preserved to complete the join and update the materialized view.

Maintaining data stream history for generating materialized views

The maintenance of a materialized view created and managed by a materialized view management platform may be dynamically adjusted. The performance of updates to a materialized view are monitored to trigger different events to make adjustments to the performance of subsequent updates. The materialized view management platform can adequately scale to handle changes to data sources as inputs to the maintenance of the materialized view, as well as make other adjustments handle various changes in the capability of the target data store to receive updates to the materialized view (e.g., target data store outages).

Dynamically adjusting performance of materialized view maintenance

Managed materialized views may be generated from across heterogeneous data sources. A request to create a materialized view may be received and performed by a materialized view management platform, which may obtain and generate the materialized view from different data sources and store the materialized view in a target system. Changes to the data sources may be obtained at the materialized view management platform and updates to the materialized view may be determined. The materialized view in the target system may be updated.

Managed materialized views created from heterogenous data sources

Queries that reference materialized views may be rewritten to compensate for stale materialized views. A query may be received that references a materialized view. A determination may be made as to whether the materialized view is stale. For a stale materialized view, the query may be rewritten to generate a query plan that obtains changes made to base tables for the materialized view not included in the stale materialized view and considers the change(s) when generating a result for the query from the materialized view. The rewritten query may then be performed to provide a result as if the materialized view were up-to-date.

Rewriting queries to compensate for stale materialized views

Stored procedures are generated to perform incremental updates to a materialized view for a database. When a request to create a materialized view is received, one or more internal tables are created from the database. A stored procedure is generated that when executed will update the materialized view of the database. The stored procedure may obtain changes to the database that were not included in the internal tables and update the internal tables to include the obtained changes. The stored procedure may be performed automatically and in response to requests.

Stored procedures for incremental updates to internal tables for materialized views

Methods, systems, and computer-readable media for a cloud-based database for spatial data lifecycle management. A spatial database receives elements of spatial data from a plurality of clients of the distributed spatial database. An individual element of the spatial data comprises one or more location values or one or more spatial objects. The spatial database stores the plurality of elements of spatial data using a plurality of storage resources. The spatial database receives a query. The spatial database determines one or more elements of spatial data matching the query from the plurality of elements of spatial data that were stored using the plurality of storage resources.

Cloud-based database for spatial data lifecycle management

A database management system stores data for a table as a journal of transaction. The records of the journal comprise information indicative of changes applied to a document of the table. The database receives a query on a table of transactions performed on the table. In response to the query on the table of transactions, the database generates results by retrieving and projecting the records of the journal in accordance with the query. The results of the query are indicative of changes applied to the document of the table.

Journal queries of a ledger-based database

A database system may use a machine learning model creation system to create a machine learning model from data stored in the database system responsive to a request from a client. The database system may obtain an executable version of the machine learning model, based on an uncompiled hardware agnostic version of the machine learning model, according to the hardware configuration of one or more computing resources selected by the database system to perform requests to the database system that invoke the machine learning model to generate predictions.

Dynamic compilation of machine learning models based on hardware configurations

A database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may perform one or more optimization operations, as part of a query plan, to prepare the data to make it suitable for use by the machine learning model.

Integrating query optimization with machine learning model prediction

Materialized views for a database system may be automatically refreshed according to performance benefits. Materialized views may be ordered according to determined performance benefits for the materialized views indicating the performance benefit obtained when a materialized view is used to perform a query at the database system. Materialized views may be selected for refresh operations according to the ordering based on a capacity of the database system to perform refresh operations.

Automatically refreshing materialized views according to performance benefit

Match rules for rewriting queries to use materialized views may be dynamically generated by a database system. A database system may generate rules that indicate whether a given query can use a materialized view and how to rewrite the given query to use the materialized view. A query may be received and the rules may be applied to the query to determine that the query can use the materialized view and to rewrite the query to use the materialized view. The rewritten query can then be executed.

Dynamic generation of match rules for rewriting queries to use materialized views

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La Jolla, CA, United States of America

Yannis Papakonstantinou