Bigquery Insert Rows with Google Cloud API on New Page Opened from Google Analytics API

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New Page Opened from the Google Analytics API

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Bigquery Insert Rows with the Google Cloud API

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Getting Started#

This integration creates a workflow with a Google Analytics trigger and Google Cloud action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Page Opened trigger
1. Connect your Google Analytics account
2. Configure timer
3. Configure View Id
Configure the Bigquery Insert Rows action
1. Connect your Google Cloud account
2. Select a Dataset ID
3. Select a Table Name
4. Configure Rows
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Page Opened on Google Analytics

Description:Emit new event when a page is viewed

Version:0.1.0

Key:google_analytics-page-opened

View on GitHub

Google Analytics Overview#

The Google Analytics API unlocks the power of your analytics data, enabling automated access to custom reports, real-time statistics, and user behavior analysis. With it, you can extract actionable insights, push data to other platforms, and tailor your business strategy with precision. Leveraging Pipedream's serverless platform, these capabilities can be seamlessly integrated into workflows that trigger actions in other apps, send alerts, or even feed data warehouses for deeper analysis.

Trigger Code#

import analytics from "../../google_analytics.app.mjs";
import { DEFAULT_POLLING_SOURCE_TIMER_INTERVAL } from "@pipedream/platform";
import utils from "../../common/utils.mjs";

export default {
  key: "google_analytics-page-opened",
  version: "0.1.0",
  name: "New Page Opened",
  description: "Emit new event when a page is viewed",
  type: "source",
  dedupe: "unique",
  props: {
    analytics,
    db: "$.service.db",
    timer: {
      type: "$.interface.timer",
      default: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
    viewId: {
      propDefinition: [
        analytics,
        "viewId",
      ],
    },
  },
  hooks: {
    async deploy() {
      const startDate = utils.monthAgo();
      this._setStartDate(startDate);
      await this.processEvent();
    },
  },
  methods: {
    _getPageViews() {
      return this.db.get("pageViews");
    },
    _setPageViews(pageViews) {
      this.db.set("pageViews", pageViews);
    },
    _getStartDate() {
      return this.db.get("startDate");
    },
    _setStartDate(startDate) {
      this.db.set("startDate", startDate);
    },
    generateMeta(pageViews, startDate) {
      const ts = Date.now();
      return {
        id: ts,
        summary: `${pageViews} page views since ${startDate}`,
        ts,
      };
    },
    async processEvent() {
      const startDate = this._getStartDate();
      const data = {
        resource: {
          reportRequests: [
            {
              viewId: this.viewId,
              dateRanges: [
                {
                  startDate,
                  endDate: "today",
                },
              ],
              metrics: [
                {
                  expression: "ga:pageviews",
                },
              ],
            },
          ],
        },
      };
      const { data: report } = await this.analytics.queryReports(data);

      const previousPageViews = this._getPageViews();
      const pageViews = report.reports[0].data.totals[0].values[0];

      if (!previousPageViews || pageViews > previousPageViews) {
        this._setPageViews(pageViews);
        const meta = this.generateMeta(pageViews, startDate);
        this.$emit(report, meta);
      }
    },
  },
  async run() {
    await this.processEvent();
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Google Analytics	`analytics`	`app`	This component uses the Google Analytics app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`
View Id	`viewId`	`string`	ID of the view to monitor. Can be found on your Google Analytics Dashboard -> Admin -> View Setting

Trigger Authentication#

Google Analytics uses OAuth authentication. When you connect your Google Analytics account, Pipedream will open a popup window where you can sign into Google Analytics and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Google Analytics API.

Pipedream requests the following authorization scopes when you connect your account:

emailprofilehttps://www.googleapis.com/auth/analytics.edit

About Google Analytics#

Google Analytics enables you to measure and report on user activity on websites, web and mobile apps, and internet-connected devices.

Action#

Bigquery Insert Rows on Google Cloud

Description:Inserts rows into a BigQuery table. [See the docs](https://github.com/googleapis/nodejs-bigquery) and for an example [here](https://github.com/googleapis/nodejs-bigquery/blob/main/samples/insertRowsAsStream.js).

Version:0.0.3

Key:google_cloud-bigquery-insert-rows

View on GitHub

Google Cloud Overview#

The Google Cloud API opens a world of possibilities for enhancing cloud operations and automating tasks. It empowers you to manage, scale, and fine-tune various services within the Google Cloud Platform (GCP) programmatically. With Pipedream, you can harness this power to create intricate workflows, trigger cloud functions based on events from other apps, manage resources, and analyze data, all in a serverless environment. The ability to interconnect GCP services with numerous other apps enriches automation, making it easier to synchronize data, streamline development workflows, and deploy applications efficiently.

Action Code#

import googleCloud from "../../google_cloud.app.mjs";
import utils from "../../common/utils.mjs";

export default {
  name: "Bigquery Insert Rows",
  version: "0.0.3",
  key: "google_cloud-bigquery-insert-rows",
  type: "action",
  description: "Inserts rows into a BigQuery table. [See the docs](https://github.com/googleapis/nodejs-bigquery) and for an example [here](https://github.com/googleapis/nodejs-bigquery/blob/main/samples/insertRowsAsStream.js).",
  props: {
    googleCloud,
    datasetId: {
      propDefinition: [
        googleCloud,
        "datasetId",
      ],
    },
    tableId: {
      propDefinition: [
        googleCloud,
        "tableId",
        ({ datasetId }) => ({
          datasetId,
        }),
      ],
    },
    rows: {
      type: "string[]",
      label: "Rows",
      description: "The rows to insert into the table. Each row is a JSON object with column names as keys and rows as values. E.g. `{\"name\": \"John\", \"age\": 20}`",
    },
  },
  async run({ $ }) {
    const {
      datasetId,
      tableId,
      rows,
    } = this;

    if (!Array.isArray(rows)) {
      throw new Error("Rows must be an array");
    }

    const rowsParsed = rows.map(utils.parse);

    try {
      const response = await this.googleCloud
        .getBigQueryClient()
        .dataset(datasetId)
        .table(tableId)
        .insert(rowsParsed);

      $.export("$summary", `Successfully inserted ${rows.length} rows into ${datasetId}.${tableId}`);

      return response;
    } catch (error) {
      console.log(JSON.stringify(error.errors, null, 2));
      throw error;
    }
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Google Cloud	`googleCloud`	`app`	This component uses the Google Cloud app.
Dataset ID	`datasetId`	`string`	Select a value from the drop down menu.
Table Name	`tableId`	`string`	Select a value from the drop down menu.
Rows	`rows`	`string[]`	The rows to insert into the table. Each row is a JSON object with column names as keys and rows as values. E.g. `{"name": "John", "age": 20}`

Action Authentication#

Google Cloud uses API keys for authentication. When you connect your Google Cloud account, Pipedream securely stores the keys so you can easily authenticate to Google Cloud APIs in both code and no-code steps.

Create a service account in GCP and set the permissions you need for Pipedream workflows.
Generate a service account key
Download the key details in JSON format
Upload the key below.

About Google Cloud#

The Google Cloud Platform, including BigQuery