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Data Ingestion with Python Cookbook
Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook: A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process

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Profile Icon Gláucia Esppenchutz
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£20.99 £23.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (4 Ratings)
eBook May 2023 414 pages 1st Edition
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£20.99 £23.99
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Arrow left icon
Profile Icon Gláucia Esppenchutz
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£20.99 £23.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (4 Ratings)
eBook May 2023 414 pages 1st Edition
eBook
£20.99 £23.99
Paperback
£29.99
Subscription
Free Trial
Renews at £16.99p/m
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£20.99 £23.99
Paperback
£29.99
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Renews at £16.99p/m

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Data Ingestion with Python Cookbook

Introduction to Data Ingestion

Welcome to the fantastic world of data! Are you ready to embark on a thrilling journey into data ingestion? If so, this is the perfect book to start! Ingesting data is the first step into the big data world.

Data ingestion is a process that involves gathering and importing data and also storing it properly so that the subsequent extract, transform, and load (ETL) pipeline can utilize the data. To make it happen, we must be cautious about the tools we will use and how to configure them properly.

In our book journey, we will use Python and PySpark to retrieve data from different data sources and learn how to store them properly. To orchestrate all this, the basic concepts of Airflow will be implemented, along with efficient monitoring to guarantee that our pipelines are covered.

This chapter will introduce some basic concepts about data ingestion and how to set up your environment to start the tasks.

In this chapter, you will build and learn the following recipes:

  • Setting up Python and the environment
  • Installing PySpark
  • Configuring Docker for MongoDB
  • Configuring Docker for Airflow
  • Logging libraries
  • Creating schemas
  • Applying data governance in ingestion
  • Implementing data replication

Technical requirements

The commands inside the recipes of this chapter use Linux syntax. If you don’t use a Linux-based system, you may need to adapt the commands:

  • Docker or Docker Desktop
  • The SQL client of your choice (recommended); we recommend DBeaver, since it has a community-free version

You can find the code from this chapter in this GitHub repository: https://github.com/PacktPublishing/Data-Ingestion-with-Python-Cookbook.

Note

Windows users might get an error message such as Docker Desktop requires a newer WSL kernel version. This can be fixed by following the steps here: https://docs.docker.com/desktop/windows/wsl/.

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Key benefits

  • Harness best practices to create a Python and PySpark data ingestion pipeline
  • Seamlessly automate and orchestrate your data pipelines using Apache Airflow
  • Build a monitoring framework by integrating the concept of data observability into your pipelines

Description

Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.

Who is this book for?

This book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.

What you will learn

  • Implement data observability using monitoring tools
  • Automate your data ingestion pipeline
  • Read analytical and partitioned data, whether schema or non-schema based
  • Debug and prevent data loss through efficient data monitoring and logging
  • Establish data access policies using a data governance framework
  • Construct a data orchestration framework to improve data quality

Product Details

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Publication date : May 31, 2023
Length: 414 pages
Edition : 1st
Language : English
ISBN-13 : 9781837633098
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Frequently bought together


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Total £ 94.97
Exploratory Data Analysis with Python Cookbook
£37.99
Data Ingestion with Python Cookbook
£29.99
Building ETL Pipelines with Python
£26.99
Total £ 94.97 Stars icon

Table of Contents

16 Chapters
Part 1: Fundamentals of Data Ingestion Chevron down icon Chevron up icon
Chapter 1: Introduction to Data Ingestion Chevron down icon Chevron up icon
Chapter 2: Principals of Data Access – Accessing Your Data Chevron down icon Chevron up icon
Chapter 3: Data Discovery – Understanding Our Data before Ingesting It Chevron down icon Chevron up icon
Chapter 4: Reading CSV and JSON Files and Solving Problems Chevron down icon Chevron up icon
Chapter 5: Ingesting Data from Structured and Unstructured Databases Chevron down icon Chevron up icon
Chapter 6: Using PySpark with Defined and Non-Defined Schemas Chevron down icon Chevron up icon
Chapter 7: Ingesting Analytical Data Chevron down icon Chevron up icon
Part 2: Structuring the Ingestion Pipeline Chevron down icon Chevron up icon
Chapter 8: Designing Monitored Data Workflows Chevron down icon Chevron up icon
Chapter 9: Putting Everything Together with Airflow Chevron down icon Chevron up icon
Chapter 10: Logging and Monitoring Your Data Ingest in Airflow Chevron down icon Chevron up icon
Chapter 11: Automating Your Data Ingestion Pipelines Chevron down icon Chevron up icon
Chapter 12: Using Data Observability for Debugging, Error Handling, and Preventing Downtime Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
Lincoln Nascimento Jun 26, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is amazing, it helped me through daily situations and also gave me new perspectives. The mention to OpenMetadata helped to solve a problem I was facing in my current company. I recommend for new and seasoned Data Engineers.
Amazon Verified review Amazon
Om S Jul 10, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Data Ingestion with Python Cookbook" is a practical and comprehensive guide that equips data engineers and enthusiasts with the knowledge and skills to design and implement efficient data ingestion pipelines. The book seamlessly blends industry best practices with real-world examples, utilizing popular open-source tools to overcome common challenges in the field.From data schema design to creating monitored pipelines with Apache Airflow, the book covers a wide range of topics essential for building robust data ingestion processes. Readers learn to integrate data observability principles into their pipelines, ensuring data quality and facilitating troubleshooting. The book also addresses the complexities of reading various data sources and formats, offering practical solutions and techniques.Throughout the book, readers gain insights into error logging best practices, data orchestration, monitoring, and establishing data access policies through a data governance framework. With a focus on automation and efficiency, the book empowers readers to effortlessly ingest and monitor their data pipelines, laying the foundation for seamless integration with subsequent stages of the ETL process."Data Ingestion with Python Cookbook" is a valuable resource for data engineers and enthusiasts seeking a comprehensive understanding of data ingestion using popular open-source tools. Whether you're a beginner or an advanced learner, this book provides practical examples and theoretical pillars of data governance to address real-world scenarios encountered in data engineering projects.
Amazon Verified review Amazon
Vinoth Jun 28, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book covers life cycle of Data ingestion process (ingesting, monitoring & errors). Author goes in depth about Data Discovery ,Ingesting Data from Structured and Unstructured Databases, PySpark with Defined and Non-Defined Schemas, Designing Monitored Data Workflows, Airflow, Logging and Monitoring Data Ingest in Airflow, Automating Data Ingestion Pipelines, Data Observability for Debugging, Error Handling, and Preventing Downtime. Throughout the book there are lots of examples with code. Highly recommend this book for entry level and experienced data engineers.
Amazon Verified review Amazon
Rashi Garg Aug 07, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I just finished reading this book last week. Having worked in data engineering field more than 10 years now , I found this book a great resource for someone who wants to build skills as a hands-on data-engineer . It offers a practical explanation of the data ingestion lifecycle, covering aspects like data discovery, ingestion from various databases, PySpark usage with different schemas, monitored workflows design, Airflow integration, error handling. The book's focus on open-source tools and real-world scenarios equips readers with skills to build efficient pipelines, ensure data quality, and troubleshoot effectively. I really liked the last section of data observability in the book where it covers the statsd , prometheus and grafana setup.Though I would have loved to more details on another popular data engineering stack Dbt,Snowflake, Yet fundamentals of building scalable data pipelines will remain the same regardless of the tech stack. I would highly recommend this book for data engineers seeking practical guidance and understanding of data ingestion challenges and solutions.
Amazon Verified review Amazon
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