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Modern Data Architectures with Python

You're reading from   Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Length 318 pages
Edition 1st Edition
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Author (1):
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Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture FREE CHAPTER 3. Chapter 2: Understanding Data Analytics 4. Part 2: Data Engineering Toolset
5. Chapter 3: Apache Spark Deep Dive 6. Chapter 4: Batch and Stream Data Processing Using PySpark 7. Chapter 5: Streaming Data with Kafka 8. Part 3:Modernizing the Data Platform
9. Chapter 6: MLOps 10. Chapter 7: Data and Information Visualization 11. Chapter 8: Integrating Continous Integration into Your Workflow 12. Chapter 9: Orchestrating Your Data Workflows 13. Part 4:Hands-on Project
14. Chapter 10: Data Governance 15. Chapter 11: Building out the Groundwork 16. Chapter 12: Completing Our Project 17. Index 18. Other Books You May Enjoy

Solution

The solution to Problem 1 is as follows:

  1. Here, we are dropping any residual database, then creating a DataFrame and writing the DataFrame as a table:
    spark.sql(f"DROP DATABASE IF EXISTS {database_name} CASCADE;")
  2. Now, we must import our libraries:
    from pyspark.sql.types import StructField, DateType, StringType, FloatType, StructType
  3. Next, we will create our database. We are defining the location of the database; all tables will be in that location:
    database_name = "chapter_2_lab"
    spark.sql(f" CREATE DATABASE IF NOT EXISTS {database_name} LOCATION 'dbfs:/tmp/accounting_alpha' ;")
  4. Now, we can write our table. First, we will define our table’s name and the schema of the table:
    table_name = "returns_bronze"
    schema = StructType([StructField("Date", DateType(), True),
                         StructField...
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