Salesforce Com, Inc.

Salesforce, Inc.

Ravishankar Arivazhagan

Donovan Schneider

Keith Kelly

Zhongtang Cai

Wenwen Liao

Ali Sakr

Alex Gitelman

Alex Field

Puneet Gupta

Pradeep Mantha

Zuye Zheng

James Diefenderfer

Nicholas Albert Bugajski

Julian Navaratnam

Martin Desrosiers

Saloni Shah

Shaila Shree

Yaoyao Lin

Anirudh Sridhar

Jesus De Oliveira

Andrew Son Nhat Tran

Irene Fung

Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule. User/source datasets may become out of sync and one or both environments may operate on stale data. One way to resolve this problem is to define triggers that, for example, monitor for changes to datasets (or other items of interest) by analytics or other activity and automatically run dataflows, recipes, or the like that are related to the changed datasets (or other items of interest).

Updating one or more databases based on dataflow events

Methods, systems, and devices supporting querying disparate data sources are described. Querying disparate data sources may include receiving an input for data stored at a first data source from a plurality of data sources, selecting a first data connector from a plurality of data connectors, wherein the first data connector corresponds to the first data source, and identifying a first query language corresponding to the first data source from a plurality of query languages. Querying the disparate data sources may further include generating a converted query based at least in part on the first query language and retrieving the data from the first data source using the first data connector based at least in part on the converted query.

Query conversion for querying disparate data sources

The technology disclosed relates to creating and frequently updating multiple online analytic processing (OLAP) analytic databases from an online transaction processing (OLTP) transaction updatable system that includes transaction commit, rollback, and field level security capabilities. It also relates to transparently decoupling extraction from rebuilding of frequently updated OLAP analytic databases from the OLTP transaction updatable system.

Streamlined creation and updating of olap analytic databases

A method for data processing is described. The method may include configuring a connection template that defines a data extraction flow for a first data source and transmitting from an analytics application server a controller application programming interface (API) call based on a query request for data stored at the first data source. The method may further include instantiating a connector microservice based on the controller API call and the connection template, where the connector microservice is configured with a first data connector with the analytics application server and a second data connector with the first data source that is specific to the first data source. The method may further include retrieving at the analytics application server the data from the first data source based on the connector microservice.

Data interface for secure analytic data system integration

Streamlined creation and updating of OLAP analytic databases

Growing community of inventors

Belmont, CA, United States of America

Ravishankar Arivazhagan