tutorials update for use of PyPi install

Author: ronanstokes-db

tutorial/1-Introduction.py

@@ -5,26 +5,21 @@
 
 # MAGIC %md ### First Steps ###
 # MAGIC 
-# MAGIC You will need to import the data generator library to workspace in order to use it. You can find the latest releases in the [``dbldatagen``](https://github.com/databrickslabs/dbldatagen) github repository
-# MAGIC on the [releases pane](https://github.com/databrickslabs/dbldatagen/releases) 
-# MAGIC 
-# MAGIC Download the wheel for the latest release from there.
-# MAGIC 
-# MAGIC Steps:
-# MAGIC 1. In your Databricks environment, using the left hand pane to select your workspace, create a library entry
-# MAGIC for the test data generator library
-# MAGIC     1. if there's an older library already installed:
-# MAGIC         1. uninstall it from existing clusters that it is installed on
-# MAGIC         9. restart the cluster(s) to have the uninstall take effect 
-# MAGIC         9. move the library to trash
-# MAGIC     9. use the create option for the workspace folder
-# MAGIC     9. select library 
-# MAGIC     9. select Python Wheel library type (by setting the library type option to `python whl`)
-# MAGIC 9. Once the library reference is created, you can install it on your clusters. You may need to restart them
-# MAGIC to take effect
-# MAGIC     
-# MAGIC 9. You can now refer to the library in your notebooks. Note the library is a Python 3 wheel library and
-# MAGIC must be run on a compatible cluster
+# MAGIC You will need to import the data generator library in order to use it.
+# MAGIC
+# MAGIC Within a notebook, you can install the package from PyPi using `pip install` to install the
+# MAGIC package with the folling command:
+# MAGIC
+# MAGIC ```
+# MAGIC %pip install dbldatagen
+# MAGIC ```
+# MAGIC
+# MAGIC The [Installation Notes](https://databrickslabs.github.io/dbldatagen/public_docs/installation_notes.html)
+# MAGIC provides details on other mechanisms for installation.
+
+# COMMAND ----------
+
+# MAGIC %pip install dbldatagen
 
 # COMMAND ----------
 

tutorial/2-Basics.py

@@ -1,9 +1,6 @@
 # Databricks notebook source
-# MAGIC %md ### Using the Databricks Labs Data Generator ###
-# MAGIC 
-# MAGIC #### Setup ####
-# MAGIC Import the Data Generator library wheel file into your environment and attach to cluster being used to
-# MAGIC run notebook
+# DBTITLE 1,Install package from PyPi
+# MAGIC %pip install dbldatagen
 
 # COMMAND ----------
 

tutorial/3-ChangeDataCapture-example.py

@@ -1,4 +1,9 @@
 # Databricks notebook source
+# DBTITLE 1,Install package from PyPi
+# MAGIC %pip install dbldatagen
+
+# COMMAND ----------
+
 # MAGIC %md ###Change Data Capture
 
 # COMMAND ----------

tutorial/4-Generating-multi-table-data.py

@@ -1,4 +1,9 @@
 # Databricks notebook source
+# DBTITLE 1,Install package from PyPi
+# MAGIC %pip install dbldatagen
+
+# COMMAND ----------
+
 # MAGIC %md ##Multi table data generation
 # MAGIC 
 # MAGIC To illustrate multi-table data generation and use, we'll use a simplified version of telecoms billing processes.
@@ -24,7 +29,9 @@
 # MAGIC 
 # MAGIC Messages will be priced per actual counts
 # MAGIC 
-# MAGIC For simplicitity, we'll ignore the free data, messages and calls threshold in most plans and the complexitity of matching devices to customers and telecoms operators - our goal here is to show generation of join ready data, rather than full modelling of phone usage invoicing.
+# MAGIC For simplicitity, we'll ignore the free data, messages and calls threshold in most plans and the complexity
+# MAGIC of matching devices to customers and telecoms operators - our goal here is to show generation of join
+# MAGIC ready data, rather than full modelling of phone usage invoicing.
 
 # COMMAND ----------