Industry Standard Project in Retailer Domain using GCP services like GCS, BigQuery, Dataproc, Composer, GitHub, CICD
β±οΈ Length: 6.2 total hours
β 4.66/5 rating
π₯ 225 students
π September 2025 update
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- Course Overview
- Embark on a project-based journey to build a complete GCP data engineering solution for the retailer domain. This course offers hands-on experience in developing an industry-standard data pipeline, from raw ingestion to actionable insights. You’ll tackle real retail data challenges like transactional processing and customer analytics, gaining directly applicable skills for modern data roles.
- Master the entire data project lifecycle, focusing on architectural patterns and best practices for scalable, reliable, and maintainable data infrastructure on Google Cloud Platform. This program equips you with the holistic understanding to translate complex business needs into robust data engineering solutions.
- Requirements / Prerequisites
- Basic familiarity with Python programming concepts (e.g., variables, functions) is beneficial for scripting.
- Working knowledge of SQL (Structured Query Language) for data querying and manipulation is highly recommended.
- Conceptual understanding of cloud computing fundamentals will be helpful; prior GCP experience is not strictly required.
- Access to a Google Cloud Platform account (trial or personal) is essential for hands-on labs.
- Comfort with basic command-line interface (CLI) operations.
- Skills Covered / Tools Used
- Managing large-scale data lakes and staging areas using Google Cloud Storage (GCS), focusing on data organization and lifecycle policies.
- Designing and optimizing a high-performance data warehouse with Google BigQuery, including advanced schema definition, query tuning, partitioning, and clustering.
- Executing distributed data processing tasks using Google Cloud Dataproc with Apache Spark for complex transformations.
- Orchestrating intricate data workflows and scheduling pipelines using Google Cloud Composer (managed Apache Airflow) with advanced DAG development and error handling.
- Implementing professional version control with GitHub for collaborative development, covering branching strategies and pull requests.
- Establishing end-to-end CI/CD (Continuous Integration/Continuous Deployment) pipelines for data solutions, automating testing, building, and deployment.
- Applying practical data modeling techniques specific to the retail sector, transforming raw transactional data into optimized analytical models.
- Developing metadata-driven pipelines for increased flexibility and maintainability, adapting to schema evolution.
- Integrating comprehensive monitoring, logging, and alerting mechanisms to ensure pipeline health and proactive issue resolution.
- Understanding data governance and security best practices on GCP for handling sensitive retail customer information.
- Benefits / Outcomes
- Confidently design, build, and deploy production-ready data engineering solutions on GCP, specifically for the retailer domain.
- Gain hands-on proficiency with a critical suite of GCP data services, enabling selection and implementation of appropriate tools.
- Develop a strong professional portfolio piece by completing a comprehensive retailer data project, showcasing end-to-end capabilities.
- Master modern development practices like CI/CD and GitHub for data solutions, enhancing employability in demanding data roles.
- Elevate problem-solving skills in data integration, transformation, and orchestration, preparing for advanced challenges.
- Position yourself as a skilled GCP Data Engineer, ready to contribute to data-driven decision-making and build scalable platforms.
- PROS
- Directly Applicable Project: Focuses on a complete, real-world retailer domain project for practical experience.
- Comprehensive GCP Toolkit: Integrates core GCP services (GCS, BigQuery, Dataproc, Composer) for holistic learning.
- Modern Development Practices: Strong emphasis on GitHub and CI/CD for automated, reliable deployments.
- Advanced Techniques Covered: Explores concepts like SCD2, incremental loading, metadata-driven design, and Medallion Architecture.
- Updated & Highly Rated Content: Ensures relevance and quality with a recent September 2025 update and excellent ratings.
- Career-Oriented: Builds a portfolio-ready project and essential skills for in-demand cloud data engineering roles.
- CONS
- The fast-paced, project-driven format and technical depth may challenge individuals without prior foundational experience in programming, SQL, or basic cloud concepts.
Learning Tracks: English,IT & Software,Other IT & Software
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