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.
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.
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.
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.
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.
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.
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
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.
CSV is a universally accepted and easy-to-read format to interpret data, and PostgreSQL is one of the most popular relational databases. Read on to learn how to export PostgreSQL as a CSV file using the COPY command, \copy command, and n8n.
PostgreSQL remains one of the most popular relational database options among data enthusiasts. Read on to learn how to import a CSV file into PostgreSQL using pgAdmin, SQL statements, and n8n.
Learn what database activity monitoring is, why it's important, and how to automatically monitor a Postgres database containing IoT data with n8n workflows.
Learn how to build an n8n workflow that processes text, stores data in two databases, and sends messages to Slack.
Looking to integrate Google Cloud Natural Language and Postgres in your company?
Build complex workflows, really fast
