Google Cloud Certified Professional Data Engineer

Theory, Hand-ons and 252 Questions, Answers with Explanations. All Hands-Ons in 1-Click Copy-Paste Style. PDF Downloads
⏱️ Length: 25.4 total hours
⭐ 4.16/5 rating
👥 49,207 students
🔄 January 2024 update

Add-On Information:

Get Instant Notification of New Courses on our Telegram channel.

Note➛ Make sure your 𝐔𝐝𝐞𝐦𝐲 cart has only this course you're going to enroll it now, Remove all other courses from the 𝐔𝐝𝐞𝐦𝐲 cart before Enrolling!

Course Overview
- This comprehensive course is meticulously designed to prepare you for the Google Cloud Certified Professional Data Engineer examination, focusing on the critical skills required to design, build, operationalize, secure, and monitor data processing systems on Google Cloud. You will delve into the entire lifecycle of data, from ingestion and transformation to storage, analysis, and visualization, all within the robust ecosystem of GCP.
- Embark on a journey to become a proficient data engineer, capable of creating scalable, highly available, and fault-tolerant data solutions. The curriculum balances foundational theory with advanced practical applications, ensuring a deep understanding of core principles alongside hands-on implementation strategies.
- Explore the interplay between various Google Cloud data services, learning how to select the most appropriate tools for different data processing needs, including batch processing, real-time analytics, and machine learning data pipelines. The course emphasizes architectural best practices for building robust and cost-effective data solutions.
- Gain a holistic perspective on data engineering within a cloud-native environment, addressing challenges such as data quality, security, governance, and compliance. This program is tailored for aspiring and current data professionals aiming to solidify their expertise and achieve industry-recognized certification on Google Cloud.
- Through a structured learning path, you will acquire the expertise to translate business requirements into technical data solutions, manage complex data infrastructures, and contribute significantly to data-driven decision-making within any organization leveraging Google Cloud.
Requirements / Prerequisites
- A foundational understanding of data processing concepts, including but not limited to ETL/ELT principles, batch versus streaming architectures, and data warehousing fundamentals.
- Basic proficiency in SQL is highly recommended, as much of the data manipulation and query optimization will involve SQL operations across various GCP data stores.
- Familiarity with at least one general-purpose programming language, with Python being particularly advantageous due to its widespread use in data engineering and GCP SDKs.
- While not strictly mandatory, prior exposure to fundamental cloud computing concepts (e.g., VMs, storage, networking) will provide a beneficial head start.
- An active Google Cloud Platform account is essential to participate in the practical exercises and hands-on labs, ideally utilizing the free tier to manage costs.
- A strong commitment to self-directed learning and the willingness to independently practice and troubleshoot is crucial for mastering the intricate concepts and services covered.
Skills Covered / Tools Used
- Data Ingestion & Storage: Master techniques for ingesting data using Cloud Storage, Pub/Sub for real-time streams, and Cloud IoT Core for device data. Design and implement robust data storage solutions with BigQuery for analytics, Cloud SQL for relational databases, Cloud Spanner for globally distributed databases, Firestore for NoSQL, and Memorystore for caching.
- Batch Data Processing: Develop and optimize batch data pipelines using Dataflow (powered by Apache Beam) for large-scale transformations, Dataproc for Apache Spark and Hadoop workloads, and leverage BigQuery’s native capabilities for complex SQL-based batch processing.
- Stream Data Processing: Architect and build real-time analytics solutions. Implement high-throughput data pipelines using Pub/Sub for message queuing and Dataflow for continuous stream processing, ensuring low-latency data availability for immediate insights.
- Workflow Orchestration: Learn to design, schedule, and monitor complex data workflows and ETL processes using Cloud Composer, Google Cloud’s managed Apache Airflow service, ensuring reliable and automated execution of data tasks.
- Data Governance & Security: Implement robust security measures using Cloud IAM for fine-grained access control, explore data lineage and cataloging with Data Catalog, and understand compliance best practices for sensitive data within GCP.
- Monitoring & Logging: Utilize Cloud Monitoring and Cloud Logging to observe data pipeline performance, troubleshoot errors, set up alerts, and gain operational insights into your data infrastructure.
- Performance Optimization & Cost Management: Acquire skills in optimizing BigQuery queries, Dataflow jobs, and Dataproc clusters for performance and efficiency, alongside strategies for effective cost management across various GCP data services.
- Machine Learning Data Pipelines: Understand how to prepare and transform data for machine learning models, integrating data engineering workflows with Vertex AI to create robust feature engineering and model serving pipelines.
- Infrastructure as Code (IaC) Principles: Gain conceptual understanding of provisioning and managing GCP resources programmatically using tools like Terraform or gcloud CLI commands, enhancing automation and reproducibility.
- Data Migration Strategies: Explore various approaches and tools for migrating existing on-premises or other cloud data solutions to Google Cloud, including database migration services and data transfer options.
- Data Visualization & Reporting Integration: Connect processed and aggregated data from BigQuery to visualization tools such as Looker Studio (formerly Data Studio) or Looker to create insightful dashboards and reports.
Benefits / Outcomes
- Achieve Google Cloud Professional Certification: Gain the comprehensive knowledge, practical skills, and confidence required to successfully pass the rigorous Google Cloud Professional Data Engineer certification exam, validating your expertise globally.
- Advance Your Career in Data Engineering: Position yourself as a highly sought-after professional with specialized skills in cloud-native data solutions, unlocking new career opportunities and accelerating your growth in the data industry.
- Master the GCP Data Ecosystem: Develop a profound understanding of Google Cloud’s extensive suite of data services, enabling you to design, build, and manage end-to-end data pipelines and architectures efficiently.
- Build Production-Ready Data Solutions: Acquire the hands-on experience to transform theoretical concepts into practical, scalable, secure, and cost-effective data solutions that address real-world business challenges.
- Strategic Decision-Making for Data: Learn to critically evaluate diverse data requirements and strategically select the optimal GCP services, tools, and architectural patterns to deliver robust and high-performing data platforms.
- Enhanced Problem-Solving Capabilities: Develop a structured approach to troubleshooting, optimizing, and securing complex data infrastructures on Google Cloud, becoming adept at resolving critical data-related issues.
- Industry Recognition and Credibility: Earn a prestigious certification that signifies your proficiency to employers, demonstrating a commitment to professional development and a deep understanding of Google Cloud data technologies.
PROS
- Comprehensive and Exam-Focused: The course is meticulously structured to cover all objectives of the Google Cloud Professional Data Engineer exam, ensuring thorough preparation.
- Highly Practical and Hands-On: Emphasizes real-world application with numerous practical exercises, moving beyond theory to build tangible skills.
- Up-to-Date Content: Regularly refreshed to incorporate the latest updates, features, and best practices within Google Cloud Platform’s evolving data services.
- Structured Learning Path: Guides you systematically through complex topics, breaking down challenging concepts into digestible modules with clear explanations.
- Robust Assessment Tools: Includes extensive questions with detailed explanations, allowing for effective self-assessment and reinforcement of learned material.
- Large and Active Student Base: Benefit from being part of a vast community of learners, potentially leveraging discussion forums for shared insights and collaborative problem-solving.
CONS
- The breadth and depth of the material necessitate a significant time commitment and strong self-discipline for effective mastery and certification success.