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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

Building our data pipeline application

We now have our ETL app. Configuring it is more complex than the schema app but basically works the same.

Here, we define our REST APIs; each API has a name that matches the table name and a URL:

etl-jobs/etl_jobs/configuration/api_raw_data/apis.py
"""
fill in
"""

We have a tiny import section but let’s start here first:

import abc
from dataclasses import dataclass

Here we follow the same base configuration pattern. First, we create a base class and then we inherit that class, adding information. This pattern allows us to create uniformity and control the look of each class:

class restConfig(abc.ABC):
    """
    fill in
    """
    name = ""
    url = ""

We will now inherit from the base class and add information to our configuration:

...
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