with Databricks and Figma?
Retrieve the output and metadata of a single task run. See the documentation
Run a job now and return the id of the triggered run. See the documentation
The Databricks API allows you to interact programmatically with Databricks services, enabling you to manage clusters, jobs, notebooks, and other resources within Databricks environments. Through Pipedream, you can leverage these APIs to create powerful automations and integrate with other apps for enhanced data processing, transformation, and analytics workflows. This unlocks possibilities like automating cluster management, dynamically running jobs based on external triggers, and orchestrating complex data pipelines with ease.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
databricks: {
type: "app",
app: "databricks",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://${this.databricks.$auth.domain}.cloud.databricks.com/api/2.0/clusters/list`,
headers: {
Authorization: `Bearer ${this.databricks.$auth.access_token}`,
},
})
},
})
The Figma API unlocks the power to automate and integrate design workflows, enabling both designers and developers to extract assets, update designs, and manage files programmatically. By leveraging the Figma API on Pipedream, you can create automated processes that sync design updates with other tools, notify team members of changes, or feed design information into other parts of your digital ecosystem.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
figma: {
type: "app",
app: "figma",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.figma.com/v1/me`,
headers: {
Authorization: `Bearer ${this.figma.$auth.oauth_access_token}`,
},
})
},
})