Databricks_Dash_Bonus.py

from sqlalchemy.engine import create_engine

## Set up SQL Alchemy engine
engine = create_engine(
   f"databricks+connector://token:{token}@{host_name}:443/{database}",
   connect_args={
       "http_path": http_path,
   }, 
   )

## Get a full table with SQL Alchemy
sensor_table = Table("gold_all_sensors", MetaData(bind=engine), autoload=True)

## Get some distinct values on app load for filters

stmt = select([sensor_table.columns.SensorMeasurement]).distinct()
results = engine.execute(stmt).fetchall()
measurement_options = [{'label': re.sub("[(),']", "", str(i)), 'value':re.sub("[(),']", "", str(i))} for i in results]
 
## Use in a function
## ORM-based SQL Query with dynamic filters in the callback
   stmt = select([
           sensor_table.columns.MeasurementDateTime,
           sensor_table.columns.LongMovingAverage,
           sensor_table.columns.SensorLocation
           ]).where(and_(
               sensor_table.columns.SensorMeasurement == input_value
           ))
 
   ## Read data via pandas or just raw Dict/array
   ## TIPS: Always try to push the filtering/complex logic down to the system where the most data is filtered
   ## minimize data brought to client
   df = pd.read_sql_query(stmt, engine).sort_values(by=['MeasurementDateTime'])