Python For Data Engineering And Analytics
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Python for Data Engineering and Analytics
Are you ready to master the art of building efficient, scalable data pipelines with Python? Python for Data Engineering and Analytics offers a clear, practical guide to designing, automating, and optimizing data workflows that power today's data-driven organizations. This book takes you step-by-step through foundational concepts and hands-on techniques-covering data ingestion, transformation, orchestration, and advanced analytics. Learn how to handle diverse data sources, manage environments, implement robust testing, and integrate machine learning within your pipelines. Explore modern architectures like streaming, batch processing, and cloud-native deployments to build resilient systems that scale effortlessly. What makes this book stand out? It covers everything you need in one place, including: Foundations of data engineering and Python essentials Data acquisition from files, databases, APIs, and cloud storage Cleaning and transforming data at scale with Pandas, Dask, and PySpark Designing data models, managing schema evolution, and data warehousing Building, automating, and orchestrating ETL/ELT pipelines with Airflow and Prefect Working with big data and real-time streaming technologies Advanced analytics, visualization, and interactive dashboard creation Integrating machine learning into data workflows Cloud data platform architectures, serverless engineering, and cost optimization Best practices for security, governance, version control, testing, and collaboration Real-world case studies demonstrating end-to-end solutions Whether you're a data engineer, analyst, or software developer looking to expand your skillset, this book equips you with practical strategies and code examples to confidently build production-ready pipelines. Embrace modern data engineering principles and accelerate your ability to turn raw data into actionable insights. Start building scalable, reliable, and efficient data systems today-transform your data workflows and drive meaningful business outcomes with Python.
Data Engineering with Python
Author: Paul Crickard
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-10-23
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 DescriptionData 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.
97 Things Every Data Engineer Should Know
Author: Tobias Macey
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-06-11
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail