SQL-Server

Microsoft SQL Server

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Databricks

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Firebase

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Google BigQuery

FreshService

Freshservice

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Amazon Athena

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MySQL

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PostgreSQL

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PagerDuty

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MongoDB

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Redshift

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IBM DB2

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Reltio

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Mode

MonkeyLearn

Monkey Learn

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Google Cloud Storage

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SingleStore

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MongoDB Cloud

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Microsoft Azure Blob Storage

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MongoDB Shell

Inspects text and returns an inference of the prevailing sentiment (POSITIVE, NEUTRAL, MIXED, or NEGATIVE).

Get sentiment

Detects the key noun phrases found in the text.

List key phrases

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How to integrate MongoDB

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How to integrate PostgresSQL

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How to integrate Amazon Redshift

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Learn How to Automate Your CSV and Flat File Management

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Developers

AWS Comprehend

Natural Language Processing (NLP)

Create database

Create schema

Create a new named internal or external stage to use for loading data from files into Snowflake tables and unloading data from tables into files.

Create stage

Create table

Creates a new virtual warehouse in the system. Creating a warehouse includes specifying its size. Initial creation of a warehouse may take some time to provision the servers, unless the warehouse is set to be created initially in a SUSPENDED state.

Create warehouse

Custom query

Delete database

Removes files that have been staged (i.e. uploaded from a local file system or unloaded from a table)

Delete files from stage

Delete rows from the target table using optional query and additional (non-target) tables.

Delete rows from table

Delete a schema from the current or specified database.

Delete schema

Removes the specified named internal or external stage from the current/specified schema. The status of the files in the stage depends on the stage type.

Delete stage

Delete a table from the current or specified schema.

Delete table

Removes the specified virtual warehouse from the system.

Delete warehouse

Updates a table by inserting one or more rows into the table.

Insert rows in table

This operation can be used to query Snowflake data loading history along various dimensions within the last 7 days.

List copy history

Lists the databases for which you have access privileges, including the dropped databases that are still within the Time Travel retention period.

List databases

List files in stage

Lists the schemas for which you have access privileges, including the dropped schemas that are still within the Time Travel retention period.

List schemas

Lists the stages for which you have access privileges.

List stages

List table columns DDL

List table rows

Lists the tables for which you have access privileges, including the dropped tables that are still within the Time Travel retention period.

List tables

Lists the warehouses for which you have access privileges.

List warehouses

Load data from staged files to an existing table. The files must already be staged.

Load data to table

Unload data from a table (or query) into one or more files in a named internal stage, a named external stage or an external stage.

Unload data from table

Modifies the properties for an existing database, including changing the name of the database and changing the Time Travel data retention period (if you are using Snowflake Enterprise Edition or higher).

Update database

Updates specified rows in the target table with new values.

Update row in table

Modifies the properties for an existing schema, including renaming the schema or swapping it with another schema, and changing the Time Travel data retention period (if you are using Snowflake Enterprise Edition or higher).

Update schema

Modifies the properties for an existing named internal or external stage.

Update stage

Modifies the properties, columns, or constraints for an existing table.

Update table

Suspends or resumes a virtual warehouse, or aborts all queries (and other SQL statements) for a warehouse. Can also be used to rename or set/unset the properties for a warehouse.

Update warehouse

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Snowflake

Database

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AWS Comprehend and Snowflake integrations couldn’t be easier with the Tray platform’s robust AWS Comprehend and Snowflake connector, which connects any services without the need for separate integration tools.

AWS Comprehend and Snowflake integrations couldn’t be easier with the Tray platform’s robust AWS Comprehend and Snowflake connectors, which can connect to any service without the need for separate integration tools.

Integrate AWS Comprehend and Snowflake with any SaaS application or service using the Tray platform’s generic connectivity options, such as the HTTP Client, Webhook Trigger, and our Connector Builder. Configure them to connect to AWS Comprehend and Snowflake and any other API, database, webhook, and more.

AWS Comprehend and Snowflake integration + automation

Popular AWS Comprehend and Snowflake integrations

AWS Comprehend and Snowflake Connector Details

AWS Comprehend

Snowflake

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Similar connectors to AWS Comprehend and Snowflake