How to load data from Trello to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Trello data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Trello
Begin by exporting your Trello board data. Trello allows you to export board data in JSON format. Navigate to the Trello board you want to export, click on the menu button (three dots) in the upper right corner, select "More," and then "Print and Export." Choose to export as JSON. Save the file to your local machine.
Step 2: Prepare JSON Data for Import
After downloading the JSON file, review its structure to understand the data format. You might want to clean or transform the data to fit your needs. Use a text editor or a JSON editor to inspect the file. Ensure the data is structured correctly for your use case in Databricks.
Step 3: Set Up Databricks Environment
Access your Databricks account and create a new Lakehouse or use an existing one. If needed, set up a new cluster or ensure an existing cluster is running. This environment will be used to process and manage your Trello data.
Step 4: Upload JSON File to Databricks File System (DBFS)
Use the Databricks interface to upload the JSON file to the Databricks File System. In the Databricks workspace, go to the "Data" tab, click on "Add Data," and select "Upload File." Choose the JSON file and upload it. The file will be stored in DBFS, ready for processing.
Step 5: Load JSON Data into a DataFrame
Create a new notebook in Databricks to load the JSON data. Use PySpark or Scala to read the JSON file from DBFS into a DataFrame. For example, in PySpark, you can use the following command:
```python
df = spark.read.json("/mnt/your-folder/your-file.json")
```
Step 6: Transform Data for Lakehouse Schema
Process the DataFrame to match your Lakehouse schema. This may involve selecting specific fields, renaming columns, or performing data transformations. Use DataFrame operations to clean and prepare the data as needed:
```python
df = df.select("field1", "field2").withColumnRenamed("field1", "new_name1")
```
Step 7: Save Data to Databricks Lakehouse
Finally, write the transformed DataFrame to your Databricks Lakehouse. You can save it in a format suitable for your analytics needs, such as Parquet, Delta, or CSV. For example, to write as a Delta table:
```python
df.write.format("delta").save("/mnt/your-folder/delta-table-name")
```
By following these steps, you can manually move data from Trello to Databricks Lakehouse without relying on third-party connectors or integrations.