Book Image

Data Engineering with Python

By : Paul Crickard
Book Image

Data Engineering with Python

By: Paul Crickard

Overview of this book

Data engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.

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Table of Contents (21 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Building Data Pipelines – Extract Transform, and Load
Section 1: Building Data Pipelines – Extract Transform, and Load
Free Chapter
2
Chapter 1: What is Data Engineering?
Chapter 1: What is Data Engineering?
What data engineers do
Data engineering versus data science
Data engineering tools
Summary
3
Chapter 2: Building Our Data Engineering Infrastructure
Chapter 2: Building Our Data Engineering Infrastructure
Installing and configuring Apache NiFi
Installing and configuring Apache Airflow
Installing and configuring Elasticsearch
Installing and configuring Kibana
Installing and configuring PostgreSQL
Installing pgAdmin 4
Summary
4
Chapter 3: Reading and Writing Files
Chapter 3: Reading and Writing Files
Writing and reading files in Python
Building data pipelines in Apache Airflow
Handling files using NiFi processors
Summary
5
Chapter 4: Working with Databases
Chapter 4: Working with Databases
Inserting and extracting relational data in Python
Inserting and extracting NoSQL database data in Python
Building data pipelines in Apache Airflow
Handling databases with NiFi processors
Summary
6
Chapter 5: Cleaning, Transforming, and Enriching Data
Chapter 5: Cleaning, Transforming, and Enriching Data
Performing exploratory data analysis in Python
Handling common data issues using pandas
Cleaning data using Airflow
Summary
7
Chapter 6: Building a 311 Data Pipeline
Chapter 6: Building a 311 Data Pipeline
Building the data pipeline
Building a Kibana dashboard
Summary
8
Section 2:Deploying Data Pipelines in Production
Section 2:Deploying Data Pipelines in Production
9
Chapter 7: Features of a Production Pipeline
Chapter 7: Features of a Production Pipeline
Staging and validating data
Building idempotent data pipelines
Building atomic data pipelines
Summary
10
Chapter 8: Version Control with the NiFi Registry
Chapter 8: Version Control with the NiFi Registry
Installing and configuring the NiFi Registry
Using the Registry in NiFi
Versioning your data pipelines
Using git-persistence with the NiFi Registry
Summary
11
Chapter 9: Monitoring Data Pipelines
Chapter 9: Monitoring Data Pipelines
Monitoring NiFi using the GUI
Monitoring NiFi with processors
Using Python with the NiFi REST API
Summary
12
Chapter 10: Deploying Data Pipelines
Chapter 10: Deploying Data Pipelines
Finalizing your data pipelines for production
Using the NiFi variable registry
Deploying your data pipelines
Summary
13
Chapter 11: Building a Production Data Pipeline
Chapter 11: Building a Production Data Pipeline
Creating a test and production environment
Building a production data pipeline
Deploying a data pipeline in production
Summary
14
Section 3:Beyond Batch – Building Real-Time Data Pipelines
Section 3:Beyond Batch – Building Real-Time Data Pipelines
15
Chapter 12: Building a Kafka Cluster
Chapter 12: Building a Kafka Cluster
Creating ZooKeeper and Kafka clusters
Testing the Kafka cluster
Summary
16
Chapter 13: Streaming Data with Apache Kafka
Chapter 13: Streaming Data with Apache Kafka
Understanding logs
Understanding how Kafka uses logs
Building data pipelines with Kafka and NiFi
Differentiating stream processing from batch processing
Producing and consuming with Python
Summary
17
Chapter 14: Data Processing with Apache Spark
Chapter 14: Data Processing with Apache Spark
Installing and running Spark
Installing and configuring PySpark
Processing data with PySpark
Summary
18
Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark
Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark
Setting up MiNiFi
Building a MiNiFi task in NiFi
Summary
19
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Appendix
Building a NiFi cluster
The basics of NiFi clustering
Building a NiFi cluster
Building a distributed data pipeline
Managing the distributed data pipeline
Summary

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Next, pass the arguments dictionary to DAG().”

A block of code is set as follows:

import datetime as dt from datetime import timedelta
from airflow import DAG from airflow.operators.bash_operator import BashOperator from airflow.operators.python_operator import PythonOperator
import pandas as pd

Any command-line input or output is written as follows:

# web properties #
nifi.web.http.port=9300

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: “Click on DAG and select Tree View.”

Tips or important notes

Appear like this.

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