How to build a data pipeline in Databricks

For a long term, I thought there was no pipeline concept in Databricks. For the most engineers they will write the whole script into one notebook rather than split into several activities like in Data factory. In this case, we have to rewirte everything in the script when the next pipeline coming.

But recently, I found there was indeed a way to achieve pipeline in databricks. Here is the document. And follows my understanding.

  • Create widgets as parameters in callee notebook
dbutils.widgets.text("input", "default") # define a text widget in callee notebook
......
dbutils.notebook.exit(defaultText) # set return variable
  • Call callee notebook through caller notebook(we can write our pipeline in this caller notebook, then call all following activity notebooks)
status = dbutils.notebook.run("pipeline",30,{"input":"pass success"}) # transfer input = “pass success” to a notebook called pipeline
  • Callee notebook can return a string to indicate the execution statues( you still remember dbutils.notebook.exit in callee notebook right?)
returned_string = dbutils.notebook.run("pipeline2", 60) 
  • The data exchange between two notebooks can be done with global tempview which locates in memory.
# in callee
sqlContext.range(10).toDF("value").createOrReplaceGlobalTempView("my_data")
dbutils.notebook.exit("my_data")

# in caller
returned_table = dbutils.notebook.run("pipeline2", 60)
global_temp_db = spark.conf.get("spark.sql.globalTempDatabase")
display(table(global_temp_db + "." + returned_table))
  • We can also call function in another notebook, which means we can build a library for all notebook
%run ./aclaraLibary # in this notebook, there are two functions which are overloaded.
simple(1,2)
simple(1,2,3)