Data Engineering With GCP

1. Foundations of Data Engineering on GCP:

   • Acquire a solid understanding of data engineering concepts, methodologies, and their applications.
   • Explore the core components of GCP, including BigQuery, Cloud Storage, and Cloud Dataprep, as the foundation for data engineering projects.

2. Data Ingestion and Integration:

   • Learn best practices for data ingestion from various sources using GCP services such as Cloud Storage, Cloud Pub/Sub, and Dataflow.
   • Explore techniques for integrating and harmonizing diverse datasets to create unified and actionable insights.

3. Building Scalable Data Pipelines:

   • Master the art of building scalable and resilient data pipelines with GCP's Cloud Composer and Cloud Dataflow.
   • Utilize Apache Beam for stream and batch processing, ensuring optimal performance and efficiency.

4. Data Transformation and Cleaning:

   • Explore GCP tools like Cloud Dataprep and Dataflow for data transformation and cleaning.
   • Implement data wrangling techniques to ensure data quality and reliability in downstream processes.

5. Data Storage and Management:

   • Understand the various storage options provided by GCP, including Bigtable, Cloud Storage, and Cloud SQL.
   • Explore best practices for data partitioning, indexing, and optimization for efficient data storage and retrieval.

6. BigQuery for Data Warehousing:

   • Harness the power of BigQuery for building scalable and high-performance data warehouses.
   • Learn to optimize queries, partition data, and design schemas for effective data warehousing on GCP.

7. Real-time Data Processing:

   • Explore real-time data processing using GCP's Pub/Sub and Dataflow for streaming analytics.
   • Implement real-time dashboards and monitoring solutions for actionable insights.

8. Security and Compliance in Data Engineering:

   • Gain insights into securing data pipelines, managing access controls, and ensuring compliance with industry standards.
   • Understand encryption techniques and best practices for protecting sensitive data in transit and at rest.

9. Monitoring and Optimization:

   • Learn how to monitor and optimize data pipelines for performance, cost efficiency, and reliability.
   • Implement logging, monitoring, and alerting solutions using GCP's Stackdriver for proactive system management.

Prerequisites:

Participants should have a basic understanding of data engineering concepts, programming (preferably Python or Java), and cloud computing fundamentals. Familiarity with GCP services is beneficial but not mandatory.

Who Should Enroll:

• Data engineers and developers aiming to enhance their skills in data engineering using GCP.
• IT professionals and software developers involved in building and managing data pipelines.
• Anyone interested in leveraging GCP for scalable and efficient data processing and analysis.

Delivery Format:

This course is delivered through a blend of video lectures, hands-on labs, collaborative projects, and interactive discussions. Participants will have access to GCP environments for practical exercises, fostering a dynamic and engaging learning experience.

• We check your knowledge before we start the session.

• We build foundational topics first and core topics next.

• Theory classes with a real-time case study.

• Demo on every topic.

• You will learn how to design architecture diagrams for each service.

• Mock exam on every topic you understand.

• Exam Preparation

• Interview Preparation