College Football Data
CollegeFootballData.com (CFBD) is a sports statistics and analytics website with no direct affiliation to the NCAA, its member conferences, or its member teams.
Integrate the College Football Data API with the Go API
Setup the College Football Data API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate College Football Data and Go remarkably fast. Free for developers.
Popular College Football Data and Go Actions#
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Overview of College Football Data#
The College Football Data API is a treasure trove for developers and sports analysts wanting to tap into detailed college football statistics and scores. With this API, you can fetch a wide array of data, from team stats to player metrics and game results. Harness this information to fuel apps, dashboards, and automated reporting systems, or to enrich your sports-related datasets.
Connect College Football Data#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
college_football_data: {
type: "app",
app: "college_football_data",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.collegefootballdata.com/conferences`,
headers: {
Authorization: `Bearer ${this.college_football_data.$auth.api_key}`,
"Accept": `application/json`,
},
})
},
})
Overview of Go#
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
Connect Go#
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}