Theory, Hand-ons and 252 Questions, Answers with Explanations. All Hands-Ons in 1-Click Copy-Paste Style. PDF Downloads
β±οΈ Length: 25.4 total hours
β 4.09/5 rating
π₯ 47,400 students
π January 2024 update
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Course Overview
- This course is meticulously designed to transform you into a Google Cloud Certified Professional Data Engineer, a highly sought-after expert in the evolving data landscape.
- Embark on a comprehensive journey through the core principles and advanced practices of architecting, implementing, and managing resilient, scalable, and secure data solutions on Google Cloud Platform.
- Beyond theoretical knowledge, the curriculum strongly emphasizes practical application, featuring extensive hands-on labs that simulate real-world data engineering challenges.
- Learn to harness the power of diverse Google Cloud services to efficiently ingest, process, store, and analyze massive datasets, converting raw information into strategic business intelligence.
- Gain a deep understanding of data governance, security protocols, and cost optimization strategies crucial for building compliant and economically viable data architectures in the cloud.
- The course offers rigorous preparation for the official Google Cloud Professional Data Engineer certification exam, supported by a substantial collection of practice questions, detailed answers, and clear explanations.
- Explore best practices for integrating machine learning capabilities into data pipelines, enabling predictive analytics and intelligent automation.
- Stay current with the latest advancements and feature updates in Google Cloud’s robust suite of data processing and analytics services, ensuring your skills remain cutting-edge.
- Engage with an interactive learning experience crafted for immediate skill acquisition and tangible career progression within the cloud data ecosystem.
- Discover the strategic advantage of leveraging Google Cloud for big data analytics, real-time data streaming, and efficient data warehousing modernization.
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Requirements / Prerequisites
- A foundational understanding of core data management concepts, including database types (relational/NoSQL) and data warehousing principles.
- Basic familiarity with command-line tools and shell scripting for interacting with cloud environments.
- Proficiency in at least one general-purpose programming language, such as Python or Java, is highly recommended for scripting and API interactions.
- Conceptual knowledge of cloud computing fundamentals, distributed systems, and networking basics.
- An eagerness to engage with complex technical challenges and a commitment to hands-on learning in a cloud environment.
- Access to a Google Cloud Platform account (many labs can leverage the free tier) to actively participate in practical exercises.
- A stable internet connection and a modern web browser for accessing course materials and cloud console.
- Prior exposure to SQL for data querying and manipulation will be beneficial.
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Skills Covered / Tools Used
- Data Ingestion: Mastering Cloud Pub/Sub for event-driven architectures, Storage Transfer Service for large-scale data migration, and Cloud Storage for diverse data lake solutions.
- Data Transformation & Processing: Proficiently using Dataflow (Apache Beam) for scalable batch and stream processing, Dataproc for managed Spark/Hadoop clusters, and Cloud Composer (Apache Airflow) for complex workflow orchestration.
- Advanced Data Storage: Expertise in BigQuery for petabyte-scale analytics and data warehousing, Cloud Spanner for globally distributed transactional databases, Cloud SQL for managed relational databases (PostgreSQL, MySQL, SQL Server), and Cloud Bigtable for high-throughput NoSQL workloads.
- Machine Learning Operations (MLOps): Integrating data pipelines with Vertex AI for model training, deployment, and monitoring, and preparing structured and unstructured data for machine learning consumption.
- Data Governance & Security: Implementing robust access control with Identity and Access Management (IAM), data encryption using Cloud Key Management Service (KMS), and ensuring data quality and cataloging with Dataplex and Data Catalog.
- Monitoring, Logging & Alerting: Configuring Cloud Monitoring and Cloud Logging to ensure the health, performance, and operational stability of data solutions and proactive issue resolution.
- Infrastructure as Code (IaC): Understanding principles for automating resource provisioning and management, typically involving tools like Terraform or GCP Deployment Manager.
- API & SDK Interaction: Programmatically interacting with Google Cloud services using client libraries and REST APIs for automation and custom solution development.
- Serverless Architecture: Designing and implementing highly scalable and cost-effective serverless data solutions using Cloud Functions and Cloud Run for event processing and microservices.
- Cost Optimization Strategies: Applying best practices for managing and reducing expenditure on GCP data services, including intelligent storage tiering, auto-scaling configurations, and query optimization techniques.
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Benefits / Outcomes
- Attain the globally recognized Google Cloud Certified Professional Data Engineer credential, affirming your expert-level proficiency in cloud data engineering.
- Position yourself for high-demand roles such as Data Engineer, Cloud Architect, or Big Data Specialist in leading organizations across various industries.
- Develop the practical skills and confidence to design, build, and operationalize complex, scalable, and secure data solutions on Google Cloud from concept to production.
- Become adept at leveraging a comprehensive suite of Google Cloud data services, making you an invaluable asset capable of addressing diverse data challenges.
- Significantly enhance your career prospects and earning potential in the rapidly expanding and critical field of cloud data analytics.
- Build a strong portfolio of practical project experience through numerous hands-on labs, ready to demonstrate your capabilities to prospective employers.
- Contribute directly to strategic business outcomes by enabling data-driven decision-making and innovation within your organization.
- Acquire a deep understanding of cloud-native data architectures and modern data pipeline design principles.
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PROS
- Exceptional Practicality: The innovative “1-Click Copy-Paste Style” for hands-on labs drastically simplifies environment setup, allowing learners to dive straight into practical application without configuration headaches.
- Robust Certification Preparation: Featuring an extensive collection of 252 questions complete with detailed answers and explanations, the course provides unparalleled readiness specifically for the Google Cloud Professional Data Engineer exam.
- Guaranteed Modern Relevance: The course boasts a recent January 2024 update, ensuring all content, services, and best practices align with the very latest developments and features on Google Cloud Platform.
- Proven Quality and Popularity: A high rating of 4.09/5 and a substantial student base of 47,400 underscore the course’s effectiveness, credibility, and widespread acceptance within the learning community.
- Comprehensive Learning Approach: The curriculum effectively balances theoretical foundations with practical application, ensuring a deep conceptual understanding alongside hands-on implementation skills.
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CONS
- The extensive breadth and depth of topics covered may prove challenging for individuals with absolutely no prior exposure to IT, programming, or fundamental data concepts.
Learning Tracks: English,IT & Software,IT Certifications