The distributed serverless database combining the flexibility of NoSQL with the relational querying capabilities of SQL systems.
Emit new event each time you add or remove a document from a specific collection, with the details of the document.
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Performs an arbitrary authorized GraphQL query. See docs here
Reads all documents from a given Fauna collection. See docs here
Fauna API offers a powerful serverless database solution for modern applications. Its unique capabilities allow for highly scalable, secure, and flexible data management. With Pipedream, you can harness the power of Fauna to create intricate serverless workflows that react to various triggers, manage data efficiently, and connect seamlessly with other services and APIs to automate complex tasks.
module.exports = defineComponent({
props: {
faunadb: {
type: "app",
app: "faunadb",
}
},
async run({steps, $}) {
const faunadb = require('faunadb')
const q = faunadb.query
const client = new faunadb.Client({ secret: this.faunadb.$auth.secret })
// Lists collections in the database tied to your secret key
const collectionsPaginator = await client.paginate(q.Collections())
this.collections = []
await collectionsPaginator.each(page => {
for (const collection of page) {
this.collections.push(collection.id)
}
})
},
})
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}