How to load data from Freshdesk to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Freshdesk data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Freshdesk
Begin by logging into your Freshdesk account. Navigate to the data you want to export, such as tickets, contacts, or companies. Freshdesk typically allows data export in CSV format through its built-in export functionality. Export the data and download the CSV files to your local system.
Step 2: Prepare Data for Transfer
Examine the exported CSV files to ensure they contain the appropriate data. Open the files in a spreadsheet application or a text editor to verify the data’s integrity and completeness. Make any necessary modifications, such as removing unwanted columns or rows, to ensure the data is ready for the next steps.
Step 3: Set Up Databricks Lakehouse Environment
Access your Databricks account and create a new Lakehouse if you haven't already. Set up a cluster that will process the data. Ensure that your Databricks environment is configured with the necessary permissions and resources to handle data ingestion and transformation.
Step 4: Upload CSV Files to Databricks File System (DBFS)
Use the Databricks interface or a CLI tool to upload your CSV files to the Databricks File System (DBFS). You can accomplish this by navigating to the “Data” section in Databricks, selecting “Add Data,” and then uploading the CSV files. This step ensures that your data is accessible for processing within Databricks.
Step 5: Create a Databricks Notebook for Data Processing
Within Databricks, create a new notebook to process the data. Use Apache Spark or PySpark to read the CSV files from DBFS. Write code to load the data into a Spark DataFrame, which will allow you to transform and manipulate the data as needed. Ensure that your notebook is attached to a running cluster.
Step 6: Transform and Cleanse Data
Perform necessary data transformations within the notebook to prepare the data for analysis or storage. This might include data cleansing, standardizing formats, or filtering out unnecessary information. Use Spark SQL or DataFrame operations to apply these transformations efficiently.
Step 7: Load Data into Databricks Lakehouse Tables
Once the data is transformed, write it into the Databricks Lakehouse. Use the Spark DataFrame API to save the data as Delta Lake tables, which provide optimized storage and fast query performance. Specify the appropriate schema and partitioning strategy to ensure efficient storage and retrieval.
By following these steps, you can effectively transfer data from Freshdesk to Databricks Lakehouse without relying on third-party connectors or integrations, leveraging built-in capabilities of both platforms instead.