Data Engineering With Python by Crickard, Paul -- Read -- Imperial Library of Trantor

Index

Data Engineering with Python Why subscribe? Contributors About the author About the reviewers Packt is searching for authors like you 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

Section 1: Building Data Pipelines – Extract Transform, and Load Chapter 1: What is Data Engineering?

What data engineers do

Required skills and knowledge to be a data engineer

Data engineering versus data science Data engineering tools

Programming languages Databases Data processing engines Data pipelines

Summary

Chapter 2: Building Our Data Engineering Infrastructure

Installing and configuring Apache NiFi

A quick tour of NiFi PostgreSQL driver

Installing and configuring Apache Airflow Installing and configuring Elasticsearch Installing and configuring Kibana Installing and configuring PostgreSQL Installing pgAdmin 4

A tour of pgAdmin 4

Summary

Chapter 3: Reading and Writing Files

Writing and reading files in Python

Writing and reading CSVs Reading and writing CSVs using pandas DataFrames Writing JSON with Python

Building data pipelines in Apache Airflow Handling files using NiFi processors

Working with CSV in NiFi Working with JSON in NiFi

Summary

Chapter 4: Working with Databases

Inserting and extracting relational data in Python

Inserting data into PostgreSQL

Inserting and extracting NoSQL database data in Python

Installing Elasticsearch Inserting data into Elasticsearch

Building data pipelines in Apache Airflow

Setting up the Airflow boilerplate Running the DAG

Handling databases with NiFi processors

Extracting data from PostgreSQL Running the data pipeline

Summary

Chapter 5: Cleaning, Transforming, and Enriching Data

Performing exploratory data analysis in Python

Downloading the data Basic data exploration

Handling common data issues using pandas

Drop rows and columns Creating and modifying columns Enriching data

Cleaning data using Airflow Summary

Chapter 6: Building a 311 Data Pipeline

Building the data pipeline

Mapping a data type Triggering a pipeline Querying SeeClickFix Transforming the data for Elasticsearch Getting every page Backfilling data

Building a Kibana dashboard

Creating visualizations Creating a dashboard

Summary

Section 2:Deploying Data Pipelines in Production Chapter 7: Features of a Production Pipeline

Staging and validating data

Staging data Validating data with Great Expectations

Building idempotent data pipelines Building atomic data pipelines Summary

Chapter 8: Version Control with the NiFi Registry

Installing and configuring the NiFi Registry

Installing the NiFi Registry Configuring the NiFi Registry

Using the Registry in NiFi

Adding the Registry to NiFi

Versioning your data pipelines Using git-persistence with the NiFi Registry Summary

Chapter 9: Monitoring Data Pipelines

Monitoring NiFi using the GUI

Monitoring NiFi with the status bar

Monitoring NiFi with processors Using Python with the NiFi REST API Summary

Chapter 10: Deploying Data Pipelines

Finalizing your data pipelines for production

Backpressure Improving processor groups

Using the NiFi variable registry Deploying your data pipelines

Using the simplest strategy Using the middle strategy Using multiple registries

Summary

Chapter 11: Building a Production Data Pipeline

Creating a test and production environment

Creating the databases Populating a data lake

Building a production data pipeline

Reading the data lake Scanning the data lake Inserting the data into staging Querying the staging database Validating the staging data Insert Warehouse

Deploying a data pipeline in production Summary

Section 3:Beyond Batch – Building Real-Time Data Pipelines Chapter 12: Building a Kafka Cluster

Creating ZooKeeper and Kafka clusters

Downloading Kafka and setting up the environment Configuring ZooKeeper and Kafka Starting the ZooKeeper and Kafka clusters

Testing the Kafka cluster

Testing the cluster with messages

Summary

Chapter 13: Streaming Data with Apache Kafka

Understanding logs Understanding how Kafka uses logs

Topics Kafka producers and consumers

Building data pipelines with Kafka and NiFi

The Kafka producer The Kafka consumer

Differentiating stream processing from batch processing Producing and consuming with Python

Writing a Kafka producer in Python Writing a Kafka consumer in Python

Summary

Chapter 14: Data Processing with Apache Spark

Installing and running Spark Installing and configuring PySpark Processing data with PySpark

Spark for data engineering

Summary

Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark

Setting up MiNiFi Building a MiNiFi task in NiFi Summary

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

Other Books You May Enjoy

Leave a review - let other readers know what you think

← Prev
Back
Next →

← Prev
Back
Next →