Is there a way to use Databricks to run a job to check a database every five minutes for new data to load to a DELTA lake table?

Absolutely! Databricks is a perfect tool for this scenario. Here's how you can use it to run a job every five minutes and check a database for new data to load into a Delta Lake table:

1. Setting Up the Delta Lake Table:

   Create a Delta Lake table in your Databricks workspace with the schema matching your database table. You can do this through the UI or using a CREATE TABLE statement in a notebook.

2. Writing the Python Script:

   Write a Python script that:

   • Connects to your database using the appropriate driver (e.g., JDBC for MySQL, psycopg2 for PostgreSQL).
   • Queries the database table for new data since the last run (you can store the timestamp of the last loaded record in a variable or Delta Lake table property).
   • Reads the new data into a Pandas DataFrame or Spark DataFrame.
   • Writes the DataFrame to your Delta Lake table using df.write.format("delta").mode("append").save().

3. Scheduling the Job:

   In the Databricks Jobs UI, create a new job with the following settings:

   • Task type: Python
   • Notebook or script location: Upload your Python script.
   • Cluster configuration: Choose a cluster configuration appropriate for your workload and database connection requirements.
   • Run Now: This allows you to test the script manually.
   • Schedule: Set a trigger that runs the job every five minutes. You can choose "Cron syntax" and enter */5 * * * * for five-minute execution.

4. Monitoring and Optimization:
   • Monitor the job runs in the Jobs UI to ensure it's running successfully and loading new data.
   • Consider implementing Delta Lake's ACID transactions and auto-compaction features for efficient data management.
   • Explore using Databricks Spark Streaming if your database supports real-time change data capture (CDC) for near-instantaneous data loading.

Additional Tips:

• You can store the last loaded record timestamp in a separate Delta Lake table or variable within the script to keep track of progress.
• Use Databricks notebooks for development and testing before converting your script into a production job.
• Consider error handling and logging mechanisms within your script for robust operation.

Here's an example Python script showcasing the key steps:

                    
                        import pandas as pd

                        # Database connection details
                        db_url = "jdbc:mysql://hostname:port/dbname"
                        db_user = "username"
                        db_password = "password"

                        # Last loaded record timestamp (stored in a Delta Lake table or variable)
                        last_loaded_timestamp = "..."

                        # Connect to the database
                        conn = create_engine(db_url, user=db_user, password=db_password)

                        # Query for new data since the last run
                        query = f"SELECT * FROM your_table WHERE updated_at > '{last_loaded_timestamp}'"
                        df = pd.read_sql(query, conn)

                        # Write the new data to Delta Lake
                        df.write.format("delta").mode("append").save("/path/to/delta_table")

                        # Update the last loaded record timestamp
                        last_loaded_timestamp = df.get("updated_at").max()

                        # Close the database connection
                        conn.close()
                    
                

Use code with caution. Learn more

Remember to adapt this code to your specific database schema, connection details, and Delta Lake table location.