PostgreSQL is a free and open-source relational database management system emphasizing extensibility and SQL compliance.
Emit new event when a new column is added to a table. See the documentation
Emit new event when a row is added or modified. See the documentation
Emit new event when a new row is added to a table. See the documentation
Emit new event when new rows are returned from a custom query that you provide. See the documentation
Emit new event when a new table is added to the database. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Finds a row in a table via a custom query. See the documentation
On Pipedream, you can leverage the PostgreSQL app to create workflows that automate database operations, synchronize data across platforms, and react to database events in real-time. Think handling new row entries, updating records from webhooks, or even compiling reports on a set schedule. Pipedream's serverless platform provides a powerful way to connect PostgreSQL with a variety of apps, enabling you to create tailored automation that fits your specific needs.
import postgresql from "@pipedream/postgresql"
export default defineComponent({
props: {
postgresql,
},
async run({ steps, $ }) {
// Component source code:
// https://github.com/PipedreamHQ/pipedream/tree/master/components/postgresql
const queryObj = {
text: "SELECT NOW()",
values: [], // Ignored since query does not contain placeholders
};
return await this.postgresql.executeQuery(queryObj);
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}