Integrate the Snowflake API with the BigML API

Setup the Snowflake API trigger to run a workflow which integrates with the BigML API. Pipedream's integration platform allows you to integrate Snowflake and BigML remarkably fast. Free for developers.

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Popular Ways to Connect Snowflake with BigML#

Create Batch Prediction with BigML API on New Row from Snowflake API

Snowflake + BigML

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Create Model with BigML API on New Row from Snowflake API

Snowflake + BigML

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Create Source (Remote URL) with BigML API on New Row from Snowflake API

Snowflake + BigML

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Create Batch Prediction with BigML API on New Query Results from Snowflake API

Snowflake + BigML

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Create Model with BigML API on New Query Results from Snowflake API

Snowflake + BigML

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Popular Snowflake and BigML Triggers#

New Row from the Snowflake API

Emit new event when a row is added to a table

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New Query Results from the Snowflake API

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

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Failed Task in Schema from the Snowflake API

Emit new events when a task fails in a database schema

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New Model Created from the BigML API

Emit new event for every created model. See docs here.

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New Prediction Made from the BigML API

Emit new event for every made prediction. See docs here.

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Popular Snowflake and BigML Actions#

Create Batch Prediction with the BigML API

Create a batch prediction given a Supervised Model ID and a Dataset ID. See the docs.

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Create Model with the BigML API

Create a model based on a given source ID, dataset ID, or model ID. See the docs.

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Execute SQL Query with the Snowflake API

Execute a custom Snowflake query. See our docs to learn more about working with SQL in Pipedream.

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Create Source (Remote URL) with the BigML API

Create a source with a provided remote URL that points to the data file that you want BigML to download for you. See the docs.

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Insert Multiple Rows with the Snowflake API

Insert multiple rows into a table

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Overview of Snowflake#

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.

Connect Snowflake#

import snowflake from '@pipedream/snowflake';

export default defineComponent({
  props: {
    snowflake,
  },
  async run({ $ }) {
    // Component source code:
    // https://github.com/PipedreamHQ/pipedream/tree/master/components/snowflake
    
    return this.snowflake.executeQuery({
      sqlText: `SELECT CURRENT_TIMESTAMP()`,
      binds: [],
    });
  },
});

Overview of BigML#

The BigML API offers a suite of machine learning tools that enable the creation and management of datasets, models, predictions, and more. It's a powerful resource for developers looking to incorporate machine learning into their applications. Within Pipedream, you can leverage the BigML API to automate workflows, process data, and apply predictive analytics. By connecting BigML to other apps in Pipedream, you can orchestrate sophisticated data pipelines that react to events, perform analyses, and take action based on machine learning insights.

Connect BigML#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    bigml: {
      type: "app",
      app: "bigml",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://bigml.io/andromeda/source`,
      params: {
        username: `${this.bigml.$auth.username}`,
        api_key: `${this.bigml.$auth.api_key}`,
      },
    })
  },
})

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