with Databricks and Pinterest?
Emit new events when new pins are created on a board or board section. See the docs here and here
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 Pinterest API opens a portal to interact programmatically with Pinterest's rich data, including boards, pins, and user information. By leveraging this API on Pipedream, you can automate actions like posting new pins, extracting pin data for analysis, and synchronizing Pinterest content with other platforms. The potential extends to marketing optimization, content management, and audience engagement, all automated and integrated within the Pipedream ecosystem.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
pinterest: {
type: "app",
app: "pinterest",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.pinterest.com/v5/user_account`,
headers: {
Authorization: `Bearer ${this.pinterest.$auth.oauth_access_token}`,
},
})
},
})