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Modern Data Architectures with Python

You're reading from  Modern Data Architectures with Python

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Brian Lipp Brian Lipp
Profile icon Brian Lipp

Table of Contents (19) Chapters

Preface Part 1:Fundamental Data Knowledge
Chapter 1: Modern Data Processing Architecture Chapter 2: Understanding Data Analytics Part 2: Data Engineering Toolset
Chapter 3: Apache Spark Deep Dive Chapter 4: Batch and Stream Data Processing Using PySpark Chapter 5: Streaming Data with Kafka Part 3:Modernizing the Data Platform
Chapter 6: MLOps Chapter 7: Data and Information Visualization Chapter 8: Integrating Continous Integration into Your Workflow Chapter 9: Orchestrating Your Data Workflows Part 4:Hands-on Project
Chapter 10: Data Governance Chapter 11: Building out the Groundwork Chapter 12: Completing Our Project Index Other Books You May Enjoy

Practical lab

Your team has been given a new data source to deliver Parquet files to dbfs. These files could come every minute or once daily; the rate and speed will vary. This data must be updated once every hour if any new data has been delivered.

Setup

Let’s set up our environment and create some fake data using Python.

Setting up folders

The following code can be run in a notebook. Here, I am using the shell magic to accomplish this:

%sh
rm -rf /dbfs/tmp/chapter_4_lab_test_data
rm -rf /dbfs/tmp/chapter_4_lab_bronze
rm -rf /dbfs/tmp/chapter_4_lab_silver
rm -rf /dbfs/tmp/chapter_4_lab_gold

Creating fake data

Use the following code to create fake data for our problems:

fake = Faker()
def generate_data(num):
    row = [{"name":fake.name(),
           "address":fake.address(),
           "city"...
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