Generate Match Report with Interzoid API on New Record (Instant, of Selectable Type) from Salesforce API

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New Record (Instant, of Selectable Type) from the Salesforce API

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Generate Match Report with the Interzoid API

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Getting Started#

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

Select this integration
Configure the New Record (Instant, of Selectable Type) trigger
1. Connect your Salesforce account
2. Configure timer
3. Select a Object Type
4. Optional- Select one or more Fields to Obtain
Configure the Generate Match Report action
1. Connect your Interzoid account
2. Select a Matching Algorithm Type
3. Select a Database Connection Type
4. Configure Connection String
5. Configure Table Name
6. Configure Match Column Name
7. Optional- Configure Reference
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 Record (Instant, of Selectable Type) on Salesforce

Description:Emit new event when a record of the selected object type is created. [See the documentation](https://sforce.co/3yPSJZy)

Version:0.2.0

Key:salesforce_rest_api-new-record-instant

View on GitHub

Salesforce Overview#

The Salesforce (REST API) provides a powerful platform for creating and managing customer relationships with a wide array of features like data manipulation, querying, and complex automation. With Pipedream's serverless execution, you can create workflows that automate your sales processes, sync data with other platforms, enhance customer engagement, and trigger actions based on specific events. Dive into Salesforce data, streamline lead management, track customer interactions, and push or pull data to or from Salesforce seamlessly.

Trigger Code#

import startCase from "lodash/startCase.js";
import common from "../common.mjs";

export default {
  ...common,
  type: "source",
  name: "New Record (Instant, of Selectable Type)",
  key: "salesforce_rest_api-new-record-instant",
  description: "Emit new event when a record of the selected object type is created. [See the documentation](https://sforce.co/3yPSJZy)",
  version: "0.2.0",
  props: {
    ...common.props,
    fieldsToObtain: {
      propDefinition: [
        common.props.salesforce,
        "fieldsToObtain",
        (c) => ({
          objType: c.objectType,
        }),
      ],
      optional: true,
      description: "Select the field(s) to be retrieved for the records. Only applicable if the source is running on a timer. If running on a webhook, or if not specified, all fields will be retrieved.",
    },
  },
  hooks: {
    ...common.hooks,
    async deploy() {
      const objectType = this.objectType;
      const nameField = await this.salesforce.getNameFieldForObjectType(objectType);
      this.setNameField(nameField);

      // emit historical events
      const { recentItems } = await this.salesforce.listSObjectTypeIds(objectType);
      const ids = recentItems.map((item) => item.Id);
      for (const id of ids.slice(-25)) {
        const object = await this.salesforce.getSObject(objectType, id);
        const event = {
          body: {
            "New": object,
            "UserId": id,
          },
        };
        this.processWebhookEvent(event);
      }
    },
  },
  methods: {
    ...common.methods,
    generateTimerMeta(item, fieldName) {
      const { objectType } = this;
      const {
        CreatedDate: createdDate,
        [fieldName]: name,
        Id: id,
      } = item;
      const entityType = startCase(objectType);
      const summary = `New ${entityType} created: ${name ?? id}`;
      const ts = Date.parse(createdDate);
      return {
        id,
        summary,
        ts,
      };
    },
    generateWebhookMeta(data) {
      const nameField = this.getNameField();
      const { New: newObject } = data.body;
      const {
        CreatedDate: createdDate,
        Id: id,
        [nameField]: name,
      } = newObject;
      const entityType = startCase(this.objectType).toLowerCase();
      const summary = `New ${entityType} created: ${name ?? id}`;
      const ts = Date.parse(createdDate);
      return {
        id,
        summary,
        ts,
      };
    },
    getEventType() {
      return "new";
    },
    async processTimerEvent(eventData) {
      const {
        paginate,
        objectType,
        setLatestDateCovered,
        getObjectTypeColumns,
        getNameField,
        generateTimerMeta,
        $emit: emit,
      } = this;

      const {
        startTimestamp,
        endTimestamp,
      } = eventData;

      const fieldName = getNameField();
      const columns = getObjectTypeColumns();

      const events = await paginate({
        objectType,
        startTimestamp,
        endTimestamp,
        columns,
      });

      const [
        latestEvent,
      ] = events;

      if (latestEvent?.CreatedDate) {
        const latestDateCovered = new Date(latestEvent.CreatedDate);
        latestDateCovered.setSeconds(0);
        setLatestDateCovered(latestDateCovered.toISOString());
      }

      Array.from(events)
        .reverse()
        .forEach((item) => {
          const meta = generateTimerMeta(item, fieldName);
          emit(item, meta);
        });
    },
    async timerActivateHook() {
      const {
        objectType,
        getObjectTypeDescription,
        setObjectTypeColumns,
      } = this;

      let columns = this.fieldsToObtain;
      if (!columns?.length) {
        const { fields } = await getObjectTypeDescription(objectType);
        columns = fields.map(({ name }) => name);
      }

      setObjectTypeColumns(columns);
    },
  },
};

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
Salesforce	`salesforce`	`app`	This component uses the Salesforce app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
N/A	`http`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.
	`timer`	`$.interface.timer`	The timer is only used as a fallback if instant event delivery (webhook) is not available.
Object Type	`objectType`	`string`	Select a value from the drop down menu.
Fields to Obtain	`fieldsToObtain`	`string[]`	Select a value from the drop down menu.

Trigger Authentication#

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

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

About Salesforce#

Web services API for interacting with Salesforce

Action#

Generate Match Report on Interzoid

Description:Generate a Match Report using a dataset table or file (CSV/TSV/Excel). [See the documentation](https://connect.interzoid.com/data-matching-workflow)

Version:0.0.1

Key:interzoid-generate-match-report

View on GitHub

Interzoid Overview#

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.

Action Code#

import interzoid from "../../interzoid.app.mjs";
import constants from "../../common/constants.mjs";

export default {
  key: "interzoid-generate-match-report",
  name: "Generate Match Report",
  description: "Generate a Match Report using a dataset table or file (CSV/TSV/Excel). [See the documentation](https://connect.interzoid.com/data-matching-workflow)",
  version: "0.0.1",
  type: "action",
  props: {
    interzoid,
    category: {
      type: "string",
      label: "Matching Algorithm Type",
      description: "This category type indicates which set of Machine Learning and matching algorithms to make use of based on type of data content",
      options: constants.CATEGORY_OPTIONS,
    },
    source: {
      type: "string",
      label: "Database Connection Type",
      description: "Source of data, such as 'CSV', 'Snowflake', 'Postgres', etc.",
      options: constants.SOURCE_OPTIONS,
    },
    connection: {
      type: "string",
      label: "Connection String",
      description: "Connection string to access database, or in the case of a CSV or TSV file, use the full URL of the location of the file.",
    },
    table: {
      type: "string",
      label: "Table Name",
      description: "Table name to access the source data. Use \"CSV\" or \"TSV\" for delimited text files.",
    },
    column: {
      type: "string",
      label: "Match Column Name",
      description: "Column name within the table to access the source data. This is a number for CSV or TSV files, starting with number 1 from the left side of the file.",
    },
    reference: {
      type: "string",
      label: "Reference",
      description: "An additional column from the source table to display in the output results, such as a primary key.",
      optional: true,
    },
  },
  async run({ $ }) {
    const response = await this.interzoid.generateMatchReport({
      $,
      params: {
        category: this.category,
        source: this.source,
        connection: this.connection,
        table: this.table,
        column: this.column,
        reference: this.reference,
      },
    });

    $.export("$summary", "Successfully generated match report");

    return response;
  },
};

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
Interzoid	`interzoid`	`app`	This component uses the Interzoid app.
Matching Algorithm Type	`category`	`string`	Select a value from the drop down menu:`{ "label": "Company and Organization Names", "value": "company" }{ "label": "Individual Names", "value": "individual" }{ "label": "Street Addresses", "value": "address" }`
Database Connection Type	`source`	`string`	Select a value from the drop down menu:`AWS RDS/AuroraSnowflakeAzure SQLGoogle Cloud SQLDatabricksPostgreSQLMySQLMariaDBParquetCSVTSVExcel`
Connection String	`connection`	`string`	Connection string to access database, or in the case of a CSV or TSV file, use the full URL of the location of the file.
Table Name	`table`	`string`	Table name to access the source data. Use "CSV" or "TSV" for delimited text files.
Match Column Name	`column`	`string`	Column name within the table to access the source data. This is a number for CSV or TSV files, starting with number 1 from the left side of the file.
Reference	`reference`	`string`	An additional column from the source table to display in the output results, such as a primary key.

Action Authentication#

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

About Interzoid#

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.