کشف طراحی کاربردی

Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book Description 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. What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. Table of Contents What is Data Engineering? Building Our Data Engineering Infrastructure Reading and Writing Files Working with Databases Cleaning, Transforming, and Enriching Data Building a 311 Data Pipeline Features of a Production Pipeline Version Control Using the NiFi Registry Monitoring and Logging Pipelines Deploying your Pipelines Building a Production Data Pipeline Building a Kafka Cluster Streaming Data with Apache Kafka Data Processing with Apache Spark Real-Time Edge Data with MiNiFi, Kafka, and Spark Appendix Cover Title Page Copyright and Credits About Packt Contributors Table of Contents Preface Section 1: Building Data Pipelines – Extract Transform, and Load Chapter 01: 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 02: 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 03: 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 04: 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 05: 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 06: 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 07: 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 08: 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 09: 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 Index **Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects** * Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples * Design data models and learn how to extract, transform, and load (ETL) data using Python * Schedule, automate, and monitor complex data pipelines in production 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. 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. * Understand how data engineering supports data science workflows * Discover how to extract data from files and databases and then clean, transform, and enrich it * Configure processors for handling different file formats as well as both relational and NoSQL databases * Find out how to implement a data pipeline and dashboard to visualize results * Use staging and validation to check data before landing in the warehouse * Build real-time pipelines with staging areas that perform validation and handle failures * Get to grips with deploying pipelines in the production environment This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. 1. What is Data Engineering? 2. Building Our Data Engineering Infrastructure 3. Reading and Writing Files 4. Working with Databases 5. Cleaning, Transforming, and Enriching Data 6. Building a 311 Data Pipeline 7. Features of a Production Pipeline 8. Version Control Using the NiFi Registry 9. Monitoring and Logging Pipelines 10. Deploying your Pipelines 11. Building a Production Data Pipeline 12. Building a Kafka Cluster 13. Streaming Data with Apache Kafka 14. Data Processing with Apache Spark 15. Real-Time Edge Data with MiNiFi, Kafka, and Spark 16. Appendix