This is the implementation of the Airtable data handler for MindsDB. Airtable is a platform that makes it easy to build powerful, custom applications. These tools can streamline just about any process, workflow, or project. And best of all, you can build them without ever learning to write a single line of code.

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Prerequisites

Before proceeding, ensure the following prerequisites are met:
  1. Install MindsDB locally via Docker or use MindsDB Cloud.
  2. To connect Airtable to MindsDB, install the required dependencies following this instruction.
  3. Install or ensure access to Airtable.

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Implementation

This handler is implemented using duckdb, a library that allows SQL queries to be executed on pandas DataFrames. In essence, when querying a particular table, the entire table is first pulled into a pandas DataFrame using the Airtable API. Once this is done, SQL queries can be run on the DataFrame using duckdb. The required arguments to establish a connection are as follows:
  • base_id is the Airtable base ID.
  • table_name is the Airtable table name.
  • api_key is the API key for the Airtable API.

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Usage

In order to make use of this handler and connect to the Airtable database in MindsDB, the following syntax can be used: 
CREATE DATABASE airtable_datasource
WITH
  engine = 'airtable',
  parameters = {
    "base_id": "dqweqweqrwwqq",
    "table_name": "iris",
    "api_key": "knlsndlknslk"
  };
You can use this established connection to query your table as follows: 
SELECT *
FROM airtable_datasource.example_tbl;
At the moment, only the SELECT statement is allowed to be executed through duckdb. This, however, has no restriction on running machine learning algorithms against your data in Airtable using the CREATE PREDICTOR statement.