Upload Training Data with MonkeyLearn API on New Query Results from Snowflake API

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Trigger workflow on

New Query Results from the Snowflake API

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Upload Training Data with the MonkeyLearn API

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Getting Started#

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

Select this integration
Configure the New Query Results trigger
1. Connect your Snowflake account
2. Configure timer
3. Configure SQL Query
4. Optional- Configure Primary key
5. Optional- Configure Emit individual events
Configure the Upload Training Data action
1. Connect your MonkeyLearn account
2. Select a Classifier Id
3. Configure Data Array
4. Optional- Select a Input Duplicates Strategy
5. Optional- Select a Existing Duplicates Strategy
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 Query Results on Snowflake

Description:Run a SQL query on a schedule, triggering a workflow for each row of results

Version:0.2.2

Key:snowflake-query-results

View on GitHub

Snowflake Overview#

Snowflake offers a cloud database and related tools to help developers create robust, secure, and scalable data warehouses. See Snowflake's Key Concepts & Architecture.

Getting Started

1. Create a user, role and warehouse in Snowflake

Snowflake recommends you create a new user, role, and warehouse when you integrate a third-party tool like Pipedream. This way, you can control permissions via the user / role, and separate Pipedream compute and costs with the warehouse. You can do this directly in the Snowflake UI.

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

2. Enter those details in Pipedream

Visit https://pipedream.com/accounts. Click the button to Connect an App. Enter the required Snowflake account data.

You'll only need to connect your account once in Pipedream. You can connect this account to multiple workflows to run queries against Snowflake, insert data, and more.

3. Build your first workflow

Visit https://pipedream.com/new to build your first workflow. Pipedream workflows let you connect Snowflake with 1,000+ other apps. You can trigger workflows on Snowflake queries, sending results to Slack, Google Sheets, or any app that exposes an API. Or you can accept data from another app, transform it with Python, Node.js, Go or Bash code, and insert it into Snowflake.

Learn more at Pipedream University.

Trigger Code#

import common from "../common.mjs";
import { v4 as uuidv4 } from "uuid";

export default {
  ...common,
  type: "source",
  key: "snowflake-query-results",
  name: "New Query Results",
  // eslint-disable-next-line
  description: "Run a SQL query on a schedule, triggering a workflow for each row of results",
  version: "0.2.2",
  props: {
    ...common.props,
    sqlQuery: {
      type: "string",
      label: "SQL Query",
      description: "Run this query on a schedule, triggering the workflow for each row of results",
    },
    // Ordering props correctly
    timer: common.props.timer,
    dedupeKey: {
      type: "string",
      label: "Primary key",
      description: "The column in your query to use for de-duplication. Duplicate rows won't trigger your workflow",
      optional: true,
    },
    emitIndividualEvents: {
      propDefinition: [
        common.props.snowflake,
        "emitIndividualEvents",
      ],
    },
  },
  methods: {
    ...common.methods,
    generateMeta(data) {
      const {
        row: { [this.dedupeKey]: id = uuidv4() },
        timestamp: ts,
      } = data;
      const summary = `New event (ID: ${id})`;
      return {
        id,
        summary,
        ts,
      };
    },
    generateMetaForCollection(data) {
      const { timestamp: ts } = data;
      const id = uuidv4();
      const summary = `New event (ID: ${id})`;
      return {
        id,
        summary,
        ts,
      };
    },
    getStatement() {
      return {
        sqlText: this.sqlQuery,
      };
    },
  },
};

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
Snowflake	`snowflake`	`app`	This component uses the Snowflake app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`	Watch for changes on this schedule
SQL Query	`sqlQuery`	`string`	Run this query on a schedule, triggering the workflow for each row of results
Primary key	`dedupeKey`	`string`	The column in your query to use for de-duplication. Duplicate rows won't trigger your workflow
Emit individual events	`emitIndividualEvents`	`boolean`	Defaults to `true`, triggering workflows on each record in the result set. Set to `false` to emit records in batch (advanced)

Trigger Authentication#

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

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

About Snowflake#

A data warehouse built for the cloud

Action#

Upload Training Data on MonkeyLearn

Description:Uploads data to a classifier. This component can be used to upload new data to a classifier, to update the tags of texts that have already been uploaded, or both. [See the docs here](https://monkeylearn.com/api/v3/#upload-classifier-data)

Version:0.0.1

Key:monkeylearn-upload-training-data

View on GitHub

MonkeyLearn Overview#

MonkeyLearn is a text analysis platform that employs machine learning to extract and process data from chunks of text. By leveraging the MonkeyLearn API on Pipedream, you can automate the categorization of text, extract specific data, analyze sentiment, and more, all in real-time. This enables the development of powerful custom workflows that can analyze customer feedback, automate email processing, or provide insightful analytics on textual data from various sources.

Action Code#

import app from "../../monkeylearn.app.mjs";
import common from "../common/common.mjs";
import { ConfigurationError } from "@pipedream/platform";

export default {
  key: "monkeylearn-upload-training-data",
  name: "Upload Training Data",
  description: "Uploads data to a classifier. This component can be used to upload new data to a classifier, to update the tags of texts that have already been uploaded, or both. [See the docs here](https://monkeylearn.com/api/v3/#upload-classifier-data)",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    classifierId: {
      propDefinition: [
        app,
        "classifierId",
      ],
    },
    dataObjectArray: {
      propDefinition: [
        app,
        "dataObjectArray",
      ],
    },
    inputDuplicatesStrategy: {
      propDefinition: [
        app,
        "inputDuplicatesStrategy",
      ],
    },
    existingDuplicatesStrategy: {
      propDefinition: [
        app,
        "existingDuplicatesStrategy",
      ],
    },
  },
  async run({ $ }) {
    const {
      classifierId,
      dataObjectArray,
      inputDuplicatesStrategy,
      existingDuplicatesStrategy,
    } = this;
    const data = common.formatObjectArray(dataObjectArray);
    if (!data || !Array.isArray(data) || data.length === 0) {
      throw new ConfigurationError("Data must to be an array and must have at least one object.");
    }
    const params = {
      data,
      input_duplicates_strategy: inputDuplicatesStrategy,
      existing_duplicates_strategy: existingDuplicatesStrategy,
    };
    const response = await this.app.uploadClassifierTrainingData($, classifierId, params);
    $.export("$summary", "Successfully uploaded training data");
    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
MonkeyLearn	`app`	`app`	This component uses the MonkeyLearn app.
Classifier Id	`classifierId`	`string`	Select a value from the drop down menu.
Data Array	`dataObjectArray`	`string[]`	A list of data objects. Example: `[{ "text": "first text", "tags": ["tag_id_1", "tag_name"], "markers": ["any_marker", "other_marker"] }]`
Input Duplicates Strategy	`inputDuplicatesStrategy`	`string`	Select a value from the drop down menu:`{ "label": "Merge", "value": "merge" }{ "label": "Keep first", "value": "keep_first" }{ "label": "Keep last", "value": "keep_last" }`
Existing Duplicates Strategy	`existingDuplicatesStrategy`	`string`	Select a value from the drop down menu:`{ "label": "Overwrite", "value": "overwrite" }{ "label": "Ignore", "value": "ignore" }`