How to load data from Monday to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Monday data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Monday.com
Begin by logging into your Monday.com account. Navigate to the board or workspace containing the data you wish to export. Use the export feature available in Monday.com to download the data as a CSV file. This can typically be done by clicking on the three-dot menu on the top right of the board and selecting "Export to Excel." This will provide you with a CSV file, which is a format easily manageable for importing into other systems.
Step 2: Review and Cleanse Data
Open the exported CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for consistency and completeness. Ensure that there are no empty fields, duplicates, or formatting errors that might cause issues during the import process. Make any necessary adjustments to ensure the data is clean and ready for import.
Step 3: Prepare Data for Import
Depending on the schema and structure required by your Databricks Lakehouse, you may need to reformat or transform the data. This can include renaming columns to match the lakehouse schema, converting data types, or splitting/combining columns. Save the final, cleaned, and formatted data as a CSV file.
Step 4: Access Databricks Workspace
Log into your Databricks account and navigate to the Databricks workspace. If you do not have an account, you will need to create one and set up a new workspace. Ensure that you have the necessary permissions to upload and manage data within the workspace.
Step 5: Upload Data to Databricks File System (DBFS)
Use the Databricks UI or the Databricks CLI to upload your prepared CSV file to the Databricks File System (DBFS). In the Databricks UI, you can use the 'Data' tab, click 'Add Data', and then 'Upload File' to select and upload your CSV file. Ensure the file is uploaded to a location in DBFS that is accessible by your Databricks notebooks or jobs.
Step 6: Create a Spark DataFrame
Once your data is uploaded to DBFS, create a new notebook in Databricks. Use PySpark or Scala to read the CSV file from DBFS into a Spark DataFrame. For example, using PySpark, you can run:
```python
df = spark.read.csv("/dbfs/path/to/your/file.csv", header=True, inferSchema=True)
```
This command reads the CSV file into a DataFrame, with the first row used as headers and automatic schema inference.
Step 7: Write Data to Delta Lake
Finally, write the DataFrame to a Delta Lake table in the Databricks Lakehouse to ensure it is stored efficiently and can be queried effectively. Use the following command in your notebook:
```python
df.write.format("delta").mode("overwrite").save("/delta/path/to/your/table")
```
Replace `/delta/path/to/your/table` with the appropriate Delta Lake path for your data. This step ensures your data is now part of the Databricks Lakehouse, ready for analysis and processing.
By following these steps, you will successfully transfer data from Monday.com to a Databricks Lakehouse without using third-party connectors or integrations.