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Google Cloud Natural Language and Postgres integration

Save yourself the work of writing custom integrations for Google Cloud Natural Language and Postgres and use n8n instead. Build adaptable and scalable Analytics, Development, and Data & Storage workflows that work with your technology stack. All within a building experience you will love.

How to connect Google Cloud Natural Language and Postgres

  • Step 1: Create a new workflow
  • Step 2: Add and configure nodes
  • Step 3: Connect
  • Step 4: Customize and extend your integration
  • Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Google Cloud Natural Language and Postgres integration: Create a new workflow and add the first step

Step 2: Add and configure Google Cloud Natural Language and Postgres nodes

You can find Google Cloud Natural Language and Postgres in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Google Cloud Natural Language and Postgres nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Google Cloud Natural Language and Postgres integration: Add and configure Google Cloud Natural Language and Postgres nodes

Step 3: Connect Google Cloud Natural Language and Postgres

A connection establishes a link between Google Cloud Natural Language and Postgres (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Google Cloud Natural Language and Postgres integration: Connect Google Cloud Natural Language and Postgres

Step 4: Customize and extend your Google Cloud Natural Language and Postgres integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Google Cloud Natural Language and Postgres with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Google Cloud Natural Language and Postgres integration: Customize and extend your Google Cloud Natural Language and Postgres integration

Step 5: Test and activate your Google Cloud Natural Language and Postgres workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Google Cloud Natural Language to Postgres or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Google Cloud Natural Language and Postgres integration: Test and activate your Google Cloud Natural Language and Postgres workflow

ETL pipeline for text processing

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel.

Cron node: Schedule the workflow to run every day

Twitter node: Collect tweets

MongoDB node: Insert the collected tweets in MongoDB

Google Cloud Natural Language node: Analyse the sentiment of the collected tweets

Set node: Extract the sentiment score and magnitude

Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database

IF node: Filter tweets with positive and negative sentiment scores

Slack node: Post tweets with a positive sentiment score in a Slack channel

NoOp node: Ignore tweets with a negative sentiment score

Nodes used in this workflow

Popular Google Cloud Natural Language and Postgres workflows

ETL pipeline for text processing

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. Cron node: Schedule the workflow to run every day Twitter node: Collect tweets MongoDB node: Insert the collected tweets in MongoDB Google Cloud Natural Language node: Analyse the sentiment of the collected tweets Set node: Extract the sentiment score and magnitude Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database IF node: Filter tweets with positive and negative sentiment scores Slack node: Post tweets with a positive sentiment score in a Slack channel NoOp node: Ignore tweets with a negative sentiment score

Build your own Google Cloud Natural Language and Postgres integration

Create custom Google Cloud Natural Language and Postgres workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Google Cloud Natural Language supported actions

Analyze Sentiment

Postgres supported actions

Delete
Delete an entire table or rows in a table
Execute Query
Execute an SQL query
Insert
Insert rows in a table
Insert or Update
Insert or update rows in a table
Select
Select rows from a table
Update
Update rows in a table

FAQs

  • Can Google Cloud Natural Language connect with Postgres?

  • Can I use Google Cloud Natural Language’s API with n8n?

  • Can I use Postgres’s API with n8n?

  • Is n8n secure for integrating Google Cloud Natural Language and Postgres?

  • How to get started with Google Cloud Natural Language and Postgres integration in n8n.io?

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