Complete Data Engineering In 8 Hours
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Complete Data Engineering in 8 Hours
Author: QuickTechie | A career growth machine
language: en
Publisher: PappuPass Learning Resources
Release Date: 2025-02-02
"Complete Data Engineering in 8 Hours" is a fast-paced learning guide designed to equip both beginners and experienced professionals with the essential skills required to excel in the field of data engineering. In today's digital age, data is paramount, driving decision-making, automation, and innovation. As QuickTechie.com emphasizes, the role of a Data Engineer is increasingly vital for organizations needing to manage, process, and analyze large volumes of data effectively. This book addresses the growing need for skilled professionals who can navigate the complexities of modern data infrastructure. This book offers a structured approach, providing practical insights into core data engineering concepts. It covers essential areas such as databases, data pipelines, Extract, Transform, Load (ETL) processes, big data technologies, and cloud platforms. Unlike traditional lengthy textbooks, this guide is designed to provide a quick yet comprehensive understanding within a targeted timeframe, allowing readers to quickly grasp fundamental principles and advanced techniques. Readers can expect to follow a step-by-step learning path, mastering the art of designing, building, and scaling data systems efficiently. The book ensures readers gain practical, industry-relevant skills that can be immediately applied in a professional setting. This makes it an excellent resource for those transitioning into the field, those aiming to upskill in their current roles, or individuals preparing for data engineering job interviews. By the end of "Complete Data Engineering in 8 Hours," readers will possess the knowledge and confidence to develop, implement, and optimize data infrastructure. This will empower them to become highly valued assets in the data-driven world, capable of contributing significantly to an organization's data strategies. The book is not just a theoretical guide; it provides hands-on learning opportunities to translate theoretical knowledge into practical skills, aligning with QuickTechie.com commitment to practical, applicable technology learning.
Data Engineering with AWS
Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Key Features Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Stay up to date with a comprehensive revised chapter on Data Governance Build modern data platforms with a new section covering transactional data lakes and data mesh Book DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learn Seamlessly ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Load data into a Redshift data warehouse and run queries with ease Visualize and explore data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Build transactional data lakes using Apache Iceberg with Amazon Athena Learn how a data mesh approach can be implemented on AWS Who this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
A Practical Guide to Data Engineering
"A Practical Guide to Machine Learning and AI: Part-I" is an essential resource for anyone looking to dive into the world of artificial intelligence and machine learning. Whether you're a complete beginner or have some experience in the field, this book will equip you with the fundamental knowledge and hands-on skills needed to harness the power of these transformative technologies. In this comprehensive guide, you'll embark on an engaging journey that starts with the basics of data engineering. You'll gain a solid understanding of big data, the key roles involved, and how to leverage the versatile Python programming language for data-centric tasks. From mastering Python data types and control structures to exploring powerful libraries like NumPy and Pandas, you'll build a strong foundation to tackle more advanced concepts. As you progress, the book delves into the realm of exploratory data analysis (EDA), where you'll learn techniques to clean, transform, and extract insights from your data. This sets the stage for the heart of the book - machine learning. You'll explore both supervised and unsupervised learning, diving deep into regression, classification, clustering, and dimensionality reduction algorithms. Along the way, you'll encounter real-world examples and hands-on exercises to reinforce your understanding and apply what you've learned. But this book goes beyond just the technical aspects. It also addresses the ethical considerations surrounding machine learning, ensuring you develop a well-rounded perspective on the responsible use of these powerful tools. Whether your goal is to jumpstart a career in data science, enhance your existing skills, or simply satisfy your curiosity about the latest advancements in AI, "A Practical Guide to Machine Learning and AI: Part-I" is your comprehensive companion. Prepare to embark on an enriching journey that will equip you with the knowledge and skills to navigate the exciting frontiers of artificial intelligence and machine learning.