How to load data from Apify Dataset to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Apify Dataset data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Extract Data from Apify
Begin by accessing your Apify account and navigating to the desired dataset. Use the Apify API to extract the data. You can do this by sending an HTTP GET request to the dataset endpoint, which will return the data in a JSON format. Save this data locally or in a specified storage location.
Step 2: Transform Data to CSV Format
Once you have the JSON data, transform it into a CSV format. This can be accomplished using a scripting language like Python. Utilize libraries such as `pandas` to read the JSON data and convert it into a CSV file, which is a more universally acceptable format for data ingestion processes.
Step 3: Set Up Databricks Environment
Log into your Databricks account and set up a new cluster if you haven't already. Ensure that the cluster is running so that it can process the data. Make note of the cluster details as you'll need them for uploading and accessing the data.
Step 4: Upload CSV to Databricks File System (DBFS)
Use Databricks' built-in interface to upload your CSV file to the Databricks File System (DBFS). You can do this through the Databricks web UI by navigating to the 'Data' tab and selecting 'Add Data'. Then, upload the CSV file from your local system to DBFS.
Step 5: Create a Table in Databricks Lakehouse
With the CSV file uploaded, you need to create a table in Databricks Lakehouse. Use a Databricks notebook to execute Spark SQL commands. Start by using the `CREATE TABLE` command to define a new table schema that matches your CSV data structure.
Step 6: Load Data into the Table
Execute a `COPY INTO` command to load the data from the CSV file into the table you just created. This command will read the CSV file from DBFS and insert the data into the table, making it part of the Databricks Lakehouse.
Step 7: Verify Data Integrity and Quality
After loading the data, run queries to verify that the data is correctly ingested and maintains its integrity. Check for any discrepancies or errors in the data. Use SQL commands to run counts, checksums, or sample queries to ensure that the dataset is complete and accurate.
By following these steps, you can effectively move data from Apify to Databricks Lakehouse without relying on third-party connectors or integrations.