How to load data from Todoist to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Todoist data into Databricks Lakehouse within minutes.

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Set up a Todoist connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Todoist data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Todoist to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Access Todoist API

To extract data from Todoist, you need to access their API. Sign up or log into your Todoist account, then navigate to the Todoist Developer page to generate an API token. This token will allow you to authenticate and make requests to the Todoist API to fetch your task data.

Use a programming language like Python to send HTTP GET requests to the Todoist API endpoints. For example, you can use the `requests` library in Python to access endpoints like `https://api.todoist.com/rest/v1/tasks` to retrieve your tasks. Store the response data, which is typically in JSON format, locally on your machine as a JSON or CSV file.

Once you have the data extracted, you need to prepare it for transformation. Check the structure of the JSON data and determine the necessary fields you need to import into Databricks Lakehouse. This might involve flattening nested JSON structures or selecting specific fields.

Use Python's `pandas` library or another data processing tool to transform the data into a tabular format that can be ingested by Databricks Lakehouse. This may involve cleaning the data, renaming columns, and converting data types to match the schema you plan to use in Databricks.

After transforming the data, save it in a format that Databricks can easily ingest. Common formats include CSV, Parquet, or Delta Lake. Ensure that the data is saved in a directory that can be accessed during the upload process.

Use Databricks CLI or a Databricks notebook to upload the saved data file directly into your Databricks Lakehouse storage. If using a notebook, you can use the `dbutils.fs.cp` command to copy the file from your local machine to a DBFS (Databricks File System) location.

Once the data file is in DBFS, create a new table in Databricks Lakehouse to load the data. Use SQL commands in a Databricks notebook to create the table and load the data from the file. An example SQL command could be `CREATE TABLE todoist_data USING CSV LOCATION '/dbfs/path/to/your/datafile.csv'`. This will make your Todoist data available for analysis and processing within the Databricks environment.

By following these steps, you can manually move data from Todoist to Databricks Lakehouse without relying on third-party connectors or integrations.