☁️ این کتاب یک راهنمای عملی برای طراحی، ساخت و استقرار Data Pipelineها روی Google Cloud Platform (GCP) هست و تمرکزش روی اینه که تو رو از سطح مفاهیم پایه تا توانایی ساخت سیستم‌های داده‌ای end-to-end در محیط Cloud برسونه.

🧠 علاوه بر مهارت‌های فنی، کتاب روی تصمیم‌گیری معماری (Architectural Decisions) و آمادگی برای آزمون Professional Data Engineer هم تمرکز داره.

🚀 در این کتاب یاد میگیری

🏗️ طراحی و ساخت Data Pipelineهای end-to-end روی GCP

☁️ کار با سرویس‌های اصلی GCP مثل BigQuery، Cloud Storage و Dataproc

🔄 طراحی ETL Pipeline برای batch و streaming data

⚙️ ارکستریشن Workflowها با Cloud Composer (Airflow)

📡 پردازش real-time با Pub/Sub و Dataflow

📊 ساخت Data Warehouse و Data Lake در محیط Cloud

🤖 استفاده از Vertex AI برای اجرای ML pipelineها

📈 ساخت داشبوردهای تحلیلی با Google Data Studio

💰 مدیریت هزینه و تخمین Cost در معماری‌های داده

🧪 پیاده‌سازی CI/CD برای پروژه‌های Data Engineering

🎯 آمادگی برای آزمون Google Professional Data Engineer

📖 توضیحات کتاب

🧭 این کتاب از پایه شروع میکنه و اول مفاهیم اصلی Data Engineering رو توضیح میده، بعد کم‌کم وارد طراحی سیستم‌های واقعی روی GCP میشه.

🏗️ مسیر یادگیری به‌صورت تدریجی پیش میره؛ از ساخت Data Warehouse ساده تا پیاده‌سازی Data Lake و Pipelineهای streaming پیچیده.

☁️ در نهایت بهت یاد میده چطور سیستم‌هایی طراحی کنی که هم مقیاس‌پذیر باشن، هم قابل مدیریت، و هم اقتصادی از نظر هزینه در Cloud.

⚙️ درباره فناوری

🔥 این کتاب کل اکوسیستم داده Google Cloud رو پوشش میده:

🗄️ BigQuery برای تحلیل داده

📦 Cloud Storage برای ذخیره‌سازی

⚙️ Cloud Composer برای orchestration

🔁 Dataproc برای Hadoop-based workloads

📡 Pub/Sub برای event streaming

⚡ Dataflow برای پردازش real-time

🤖 Vertex AI برای ML pipelines

📚 درباره کتاب

🧠 تمرکز اصلی این کتاب روی اینه که فقط ابزار یاد نگیری، بلکه بفهمی چطور با اون‌ها یک معماری داده‌ای واقعی طراحی کنی.

💡 همچنین دید خوبی درباره نقش Data Engineer در محیط‌های Cloud-native بهت میده و کمک میکنه برای مصاحبه‌ها و حتی آزمون‌های رسمی آماده بشی.

🔥 محتویات اصلی کتاب

☁️ مفاهیم پایه Data Engineering در Cloud

🗄️ ساخت Data Warehouse با BigQuery

📦 ساخت Data Lake با Dataproc

⚙️ Orchestration با Cloud Composer

📡 Streaming با Pub/Sub و Dataflow

🤖 ML Pipeline با Vertex AI

📊 Data Visualization با Data Studio

💰 Cost Optimization در GCP

🧪 CI/CD در GCP

🎯 آمادگی برای Professional Data Engineer Exam

👥 این کتاب برای چه کسانی مناسبه؟

💻 برای Data Engineerها، Data Analystها و کسانی که میخوان روی GCP کار کنن یا برای آزمون Google Professional Data Engineer آماده بشن.

👨‍🏫 درباره نویسنده

🧠 آدی ویجایا یک Cloud Data Engineer در Google هست که روی طراحی سیستم‌های Big Data و Analytics در سطح جهانی کار میکنه.

🏗️ او تجربه همکاری با شرکت‌های بین‌المللی در صنایع مختلف رو داره و در توسعه راهکارهای داده‌ای مقیاس‌پذیر نقش داشته.

🎓 تحصیلات او در رشته Computer Science بوده و یکی از بنیان‌گذاران DataLabs Indonesia هم هست.

Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer

Key Features

• Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution
• Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines
• Discover tips to prepare for and pass the Professional Data Engineer exam

Book Description

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards.

Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP.

By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.

What you will learn

• Load data into BigQuery and materialize its output for downstream consumption
• Build data pipeline orchestration using Cloud Composer
• Develop Airflow jobs to orchestrate and automate a data warehouse
• Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster
• Leverage Pub/Sub for messaging and ingestion for event-driven systems
• Use Dataflow to perform ETL on streaming data
• Unlock the power of your data with Data Studio
• Calculate the GCP cost estimation for your end-to-end data solutions

Who this book is for

This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

Table of Contents

1. Fundamentals of Data Engineering
2. Big Data Capabilities on GCP
3. Building a Data Warehouse in BigQuery
4. Building Orchestration for Batch Data Loading Using Cloud Composer
5. Building a Data Lake Using Dataproc
6. Processing Streaming Data with Pub/Sub and Dataflow
7. Visualizing Data for Making Data-Driven Decisions with Data Studio
8. Building Machine Learning Solutions on Google Cloud Platform
9. User and Project Management in GCP
10. Cost Strategy in GCP
11. CI/CD on Google Cloud Platform for Data Engineers
12. Boosting Your Confidence as a Data Engineer

About the Author

Adi Wijaya is a strategic cloud data engineer at Google. He holds a bachelor's degree in computer science from Binus University and co-founded DataLabs in Indonesia. Currently, he dedicates himself to big data and analytics and has spent a good chunk of his career helping global companies in different industries.