Disclaimer
Chapter One - Google Professional Data Engineering Overview
Chapter Two - Design Data Processing Systems
Chapter Three - Building and Operationalizing a Data Processing System
Chapter Four - Ensuring Quality Solution
Chapter Five - Data Engineering on Google Cloud
Chapter Six - Preparing for a Google Cloud Exam
Chapter Seven - Data Engineering Examination
Chapter Eight - Conclusion and Outline Appreciation

Do you want to learn the skills needed to be successful in a data engineer role?
Do you want to learn about the infrastructure and platform services provided by Google Cloud Platform?
If You Answered "Yes" To Any of The Above, Look No Further. This is the book for you!
Hello! Welcome to "GOOGLE PROFESSIONAL DATA ENGINEERING".

People looking to qualify in each job market are becoming increasingly competitive, and the qualifications required for a candidate to fill a vacancy are becoming increasingly demanding. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning.

You'll also have the opportunity to practice key job skills, including designing, building, and running data processing systems; and operationalizing machine-learning models.
By the end of this book, you will be ready to use Google Cloud Data Engineering services to design, deploy and monitor data pipelines, deploy advanced database systems, build data analysis platforms, and support production machine learning environments. This book provides the skills you need to advance your career as a data engineer and provides training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification.

Preparing in advance and getting to the market as soon as possible, puts the professional closer to winning a job. Once again as IT professionals. Here's what makes this book special:
Google Professional Data Engineering Overview
Design Data Processing Systems
Building and Operationalizing A Data Processing System
Ensuring Quality Solution
Data Engineering on Google Cloud
Preparing for A Google Cloud Exam
Data Engineering Examination
Much, much more!
This book is different from others because in this book:
You will be able to move forward architecting real-world data engineering solutions
You will understand all the core services you'll need to know for the Data Engineer
You will understand how to use Google's Big Data Services on the Google Cloud Platform.
If you are interested in becoming a data engineer on Google's Cloud Platform then this book is for you. Interested? Then Scroll up, Click on "Buy now with 1-Click", and Get Your Copy Now!

Google Professional Data Engineer: A handy guide to designing, developing, and managing engineer data solutions

Preface
I. Foundations of Data Systems
1. Reliable, Scalable, and Maintainable Applications
2. Data Models and Query Languages
3. Storage and Retrieval
4. Encoding and Evolution
II. Distributed Data
5. Replication
6. Partitioning
7. Transactions
8. The Trouble with Distributed Systems
9. Consistency and Consensus
III. Derived Data
10. Batch Processing
11. Stream Processing
12. The Future of Data Systems
Glossary
Index

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
Make informed decisions by identifying the strengths and weaknesses of different tools
Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
Understand the distributed systems research upon which modern databases are built
Peek behind the scenes of major online services, and learn from their architectures

Designing Data-Intensive Applications

Preface
Acknowledgments
About This Books
1. A New Paradigm for Big Data
Part 1. Batch Layer
2. Data Model for Big Data
3. Data Model for Big Data: Illustration
4. Data Storage on the Batch Layer
5. Data Storage on the Batch Layer: Illustration
6. Batch Layer
7. Batch Layer: Illustration
8. An Example Batch Layer: Architecture and Algorithms
9. An Example Batch Layer: Implementation
Part 2. Serving Layer
10. Serving Layer
11. Serving Layer: Illustration
Part 3: Speed Layer
12. Realtime Views
13. Realtime Views: Illustration
14. Queuing and Stream Processing
15. Queuing and Stream Processing: Illustration
16. Micro-Batch Stream Processing
17. Micro-Batch Stream Processing: Illustration
18. Lambda Architecture in Depth
Index

Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.

Big Data: Principles and best practices of scalable real-time data systems

Preface
1. A (Book) Case for Eventual Consistency
2. A/B and How to Be
3. About the Storage Layer
4. Analytics as the Secret Glue for Microservice Architectures
5. Automate Your Infrastructure
6. Automate Your Pipeline Tests
7. Be Intentional About the Batching Model in Your Data Pipelines
8. Beware of Silver-Bullet Syndrome
9. Building a Career as a Data Engineer
10. Business Dashboards for Data Pipelines
11. Caution: Data Science Projects Can Turn into the Emperor’s New Clothes
12. Change Data Capture
13. Column Names as Contracts
14. Consensual, Privacy-Aware Data Collection
15. Cultivate Good Working Relationships with Data Consumers
16. Data Engineering != Spark
17. Data Engineering for Autonomy and Rapid Innovation
18. Data Engineering from a Data Scientist’s Perspective
19. Data Pipeline Design Patterns for Reusability and Extensibility
20. Data Quality for Data Engineers
21. Data Security for Data Engineers
22. Data Validation Is More Than Summary Statistics
23. Data Warehouses Are the Past, Present, and Future
24. Defining and Managing Messages in Log-Centric Architectures
25. Demystify the Source and Illuminate the Data Pipeline
26. Develop Communities, Not Just Code
27. Effective Data Engineering in the Cloud World
28. Embrace the Data Lake Architecture
29. Embracing Data Silos
30. Engineering Reproducible Data Science Projects
31. Five Best Practices for Stable Data Processing
32. Focus on Maintainability and Break Up Those ETL Tasks
33. Friends Don’t Let Friends Do Dual-Writes
34. Fundamental Knowledge
35. Getting the “Structured” Back into SQL
36. Give Data Products a Frontend with Latent Documentation
37. How Data Pipelines Evolve
38. How to Build Your Data Platform like a Product
39. How to Prevent a Data Mutiny
40. Know the Value per Byte of Your Data
41. Know Your Latencies
42. Learn to Use a NoSQL Database, but Not like an RDBMS
43. Let the Robots Enforce the Rules
44. Listen to Your Users—but Not Too Much
45. Low-Cost Sensors and the Quality of Data
46. Maintain Your Mechanical Sympathy
47. Metadata ≥ Data
48. Metadata Services as a Core Component of the Data Platform
49. Mind the Gap: Your Data Lake Provides No ACID Guarantees
50. Modern Metadata for the Modern Data Stack
51. Most Data Problems Are Not Big Data Problems
52. Moving from Software Engineering to Data Engineering
53. Observability for Data Engineers
54. Perfect Is the Enemy of Good
55. Pipe Dreams
56. Preventing the Data Lake Abyss
57. Prioritizing User Experience in Messaging Systems
58. Privacy Is Your Problem
59. QA and All Its Sexiness
60. Seven Things Data Engineers Need to Watch Out for in ML Projects
61. Six Dimensions for Picking an Analytical Data Warehouse
62. Small Files in a Big Data World
63. Streaming Is Different from Batch
64. Tardy Data
65. Tech Should Take a Back Seat for Data Project Success
66. Ten Must-Ask Questions for Data-Engineering Projects
67. The Data Pipeline Is Not About Speed
68. The Dos and Don’ts of Data Engineering
69. The End of ETL as We Know It
70. The Haiku Approach to Writing Software
71. The Hidden Cost of Data Input/Output
72. The Holy War Between Proprietary and Open Source Is a Lie
73. The Implications of the CAP Theorem
74. The Importance of Data Lineage
75. The Many Meanings of Missingness
76. The Six Words That Will Destroy Your Career
77. The Three Invaluable Benefits of Open Source for Testing Data Quality
78. The Three Rs of Data Engineering
79. The Two Types of Data Engineering and Data Engineers
80. The Yin and Yang of Big Data Scalability
81. Threading and Concurrency in Data Processing
82. Three Important Distributed Programming Concepts
83. Time (Semantics) Won’t Wait
84. Tools Don’t Matter, Patterns and Practices Do
85. Total Opportunity Cost of Ownership
86. Understanding the Ways Different Data Domains Solve Problems
87. What Is a Data Engineer? Clue: We’re Data Science Enablers
88. What Is a Data Mesh, and How Not to Mesh It Up
89. What Is Big Data?
90. What to Do When You Don’t Get Any Credit
91. When Our Data Science Team Didn’t Produce Value
92. When to Avoid the Naive Approach
93. When to Be Cautious About Sharing Data
94. When to Talk and When to Listen
95. Why Data Science Teams Need Generalists, Not Specialists
96. With Great Data Comes Great Responsibility
97. Your Data Tests Failed! Now What?
Contributors
Index

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

97 Things Every Data Engineer Should Know

Foreword
Preface
1. Introduction to Data Analysis with Spark
2. Downloading Spark and Getting Started
3. Programming with RDDs
4. Working with Key/Value Pairs
5. Loading and Saving Your Data
6. Advanced Spark Programming
7. Running on a Cluster
8. Tuning and Debugging Spark
9. Spark SQL
10. Spark Streaming
11. Machine Learning with MLlib
Index

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Learning Spark: Lightning-Fast Big Data Analysis 1st Edition

Chapter 1 Selecting Appropriate Storage Technologies
Chapter 2 Building and Operationalizing Storage Systems
Chapter 3 Designing Data Pipelines
Chapter 4 Designing a Data Processing Solution
Chapter 5 Building and Operationalizing Processing Infrastructure
Chapter 6 Designing for Security and Compliance
Chapter 7 Designing Databases for Reliability, Scalability, and Availability
Chapter 8 Understanding Data Operations for Flexibility and Portability
Chapter 9 Deploying Machine Learning Pipelines
Chapter 10 Choosing Training and Serving Infrastructure
Chapter 11 Measuring, Monitoring, and Troubleshooting Machine Learning Models
Chapter 12 Leveraging Prebuilt Models as a Service
Appendix Answers to Review Questions
Index

The proven Study Guide that prepares you for this new Google Cloud exam

The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. 

Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. 

•Build and operationalize storage systems, pipelines, and compute infrastructure

•Understand machine learning models and learn how to select pre-built models

•Monitor and troubleshoot machine learning models

•Design analytics and machine learning applications that are secure, scalable, and highly available. 

This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.

Official Google Cloud Certified Professional Data Engineer Study Guide

Front Matter
Creating an Azure Data Factory Instance
Your First Pipeline
The Copy Data Activity
Expressions
Parameters
Controlling Flow
Data Flows
Integration Runtimes
Power Query in ADF
Publishing to ADF
Triggers
Monitoring
Back Matter

Data engineers who need to hit the ground running will use this book to build skills in Azure Data Factory v2 (ADF). The tutorial-first approach to ADF taken in this book gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft’s cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components.

The hands-on introduction to ADF found in this book is equally well-suited to data engineers embracing their first ETL/ELT toolset as it is to seasoned veterans of Microsoft’s SQL Server Integration Services (SSIS). The example-driven approach leads you through ADF pipeline construction from the ground up, introducing important ideas and making learning natural and engaging. SSIS users will find concepts with familiar parallels, while ADF-first readers will quickly master those concepts through the book’s steady building up of knowledge in successive chapters. Summaries of key concepts at the end of each chapter provide a ready reference that you can return to again and again.

You will:
Create pipelines, activities, datasets, and linked services
Build reusable components using variables, parameters, and expressions
Move data into and around Azure services automatically
Transform data natively using ADF data flows and Power Query data wrangling
Master flow-of-control and triggers for tightly orchestrated pipeline execution
Publish and monitor pipelines easily and with confidence

Azure Data Factory by Example

Section 1: Building Data Pipelines - Extract Transform, and Load
Chapter 1: What is Data Engineering?
Chapter 2: Building Our Data Engineering Infrastructure
Chapter 3: Reading and Writing Files
Chapter 4: Working with Databases
Chapter 5: Cleaning, Transforming, and Enriching Data
Chapter 6: Building a 311 Data Pipeline
Section 2: Deploying Data Pipelines in Production
Chapter 7: Features of a Production Pipeline
Chapter 8: Version Control with the NiFi Registry
Chapter 9: Monitoring Data Pipelines
Chapter 10: Deploying Data Pipelines
Chapter 11: Building a Production Data Pipeline
Section 3: Beyond Batch - Building Real-Time Data Pipelines
Chapter 12: Building a Kafka Cluster
Chapter 13: Streaming Data with Apache Kafka
Chapter 14: Data Processing with Apache Spark
Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark
Other Books You May Enjoy

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.

Data Engineering with Python

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