DEAR Inventory is a comprehensive inventory control application positioned as a complete back end management system with product planning, cost and development, manufacturing, sales, shipping and payment features.
Emit new event when a purchase order is created and authorized
Emit new event when a sale order is created and authorized
Emit new event when a sale quote is created and authorized
Emit new event when the available stock level changes. See the documentation
Emit new event when a customer is updated. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
DEAR Systems offers an API that provides programmatic access to its inventory management platform, allowing for seamless integration with other apps for automating various business processes. Leveraging this API within Pipedream, you can create custom workflows to synchronize inventory levels, manage sales and purchase orders, and automate financial reporting among other tasks. By connecting DEAR Systems to Pipedream’s vast array of supported apps, you can achieve a high degree of automation, reducing manual entry and data errors, and gaining insights from real-time data processing.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
dear: {
type: "app",
app: "dear",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://inventory.dearsystems.com/ExternalApi/v2/me`,
headers: {
"api-auth-accountid": `${this.dear.$auth.account_id}`,
"api-auth-applicationkey": `${this.dear.$auth.application_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}}