Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Tutorial: Set up a Jupyter notebook in JupyterLab to test and debug ETL scripts

In this tutorial, you connect a Jupyter notebook in JupyterLab running on your local machine to a development endpoint. You do this so that you can interactively run, debug, and test Amazon Glue extract, transform, and load (ETL) scripts before deploying them. This tutorial uses Secure Shell (SSH) port forwarding to connect your local machine to an Amazon Glue development endpoint. For more information, see Port forwarding on Wikipedia.

Step 1: Install JupyterLab and Sparkmagic

You can install JupyterLab by using conda or pip. conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. pip is the package installer for Python.

If you're installing on macOS, you must have Xcode installed before you can install Sparkmagic.

Step 2: Start JupyterLab

When you start JupyterLab, your default web browser is automatically opened, and the URL http://localhost:8888/lab/workspaces/{workspace_name} is shown.

$ jupyter lab

Step 3: Initiate SSH port forwarding to connect to your development endpoint

Next, use SSH local port forwarding to forward a local port (here, 8998) to the remote destination that is defined by Amazon Glue (169.254.76.1:8998).

Step 4: Run a simple script fragment in a notebook paragraph

Now your notebook in JupyterLab should work with your development endpoint. Enter the following script fragment into your notebook and run it.

import sys
from pyspark.context import SparkContext
from awsglue.context import GlueContext
 
# Create a Glue context
glueContext = GlueContext(SparkContext.getOrCreate())
 
# Create a DynamicFrame using the 'persons_json' table
persons_DyF = glueContext.create_dynamic_frame.from_catalog(database="legislators", table_name="persons_json")
 
# Print out information about *this* data
print("Count:  ", persons_DyF.count())
persons_DyF.printSchema()

The output of the script is as follows.

 Count:  1961
 root
 |-- family_name: string
 |-- name: string
 |-- links: array
 |    |-- element: struct
 |    |    |-- note: string
 |    |    |-- url: string
 |-- gender: string
 |-- image: string
 |-- identifiers: array
 |    |-- element: struct
 |    |    |-- scheme: string
 |    |    |-- identifier: string
 |-- other_names: array
 |    |-- element: struct
 |    |    |-- note: string
 |    |    |-- name: string
 |    |    |-- lang: string
 |-- sort_name: string
 |-- images: array
 |    |-- element: struct
 |    |    |-- url: string
 |-- given_name: string
 |-- birth_date: string
 |-- id: string
 |-- contact_details: array
 |    |-- element: struct
 |    |    |-- type: string
 |    |    |-- value: string
 |-- death_date: string

Troubleshooting