Tomba
Tomba is the leading solution to find and verify professional email addresses. Start using Tomba and connect with the people that matter for your business.
Integrate the Tomba API with the Python API
Setup the Tomba API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Tomba and Python remarkably fast. Free for developers.
Popular Tomba and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Overview of Tomba #
The Tomba API is a potent tool for email discovery and domain search. With it, you can automate the process of finding and verifying email addresses linked to a domain, which could be pivotal for lead generation, outreach campaigns, or market research. By leveraging the Tomba API on Pipedream, you streamline these tasks into efficient workflows that can interact with numerous other apps and services, such as CRMs, marketing automation tools, or database managers. The synergy between Tomba and Pipedream allows for real-time processing and integration of email data within your existing business systems.
Connect Tomba #
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
tomba: {
type: "app",
app: "tomba",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.tomba.io/v1/me`,
headers: {
"X-Tomba-Key": `${this.tomba.$auth.api_key}`,
"X-Tomba-Secret": `${this.tomba.$auth.api_secret}`,
},
})
},
})
Overview of Python#
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:
Connect Python#
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}}