AI & ML-based Data Engineering in the Cloud for better data - Connect to Cloud database tables, CSVs, etc with data workloads running entirely in the Cloud.
Generate a Match Report using a dataset table or file (CSV/TSV/Excel). See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Retrieve a match score (likelihood of matching) between two individual names on a scale of 0-100. See the documentation
Retrieve a match score (likelihood of matching) from 0-100 between two organization names. See the documentation
The Interzoid API offers a plethora of data-driven APIs that enable you to enrich, standardize, and deduplicate data across various fields such as demographics, financials, and text. With these capabilities, you can enhance data quality, drive better analytics, and create more intelligent workflows and applications. In Pipedream, you can integrate these APIs into serverless workflows, triggering actions based on various events, manipulating and routing data to other apps, services, or data stores with ease.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
interzoid: {
type: "app",
app: "interzoid",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.interzoid.com/getremainingcredits`,
params: {
license: `${this.interzoid.$auth.api_key}`,
},
})
},
})
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}}