Introduction of Data Engineering with GCP

This course focuses on building robust data pipelines and solutions using Google Cloud Platform (GCP) tools and services. Participants will learn to design, implement, and optimize scalable data solutions, leveraging GCP’s suite of tools for data storage, processing, analytics, and machine learning. Through hands-on exercises, you will gain practical experience in architecting end-to-end data solutions that handle large-scale data processing and meet business requirements for real-time and batch processing.

Prerequisites

Participants should have:

• A basic understanding of cloud computing concepts and GCP services.
• Familiarity with data engineering principles and data workflows.
• Experience with programming or scripting languages, particularly Python.
• Knowledge of database systems (SQL and NoSQL).
• A foundational understanding of data storage and processing models (batch vs. streaming).

Table of Contents

1. Introduction to Data Engineering on GCP
   1.1 Overview of GCP for Data Engineering
   1.2 Core GCP Services for Data Engineers
   1.3 Understanding Data Engineering Concepts
2. Data Storage Solutions in GCP
   2.1 Cloud Storage: Object Storage and Buckets
   2.2 Cloud SQL and Cloud Spanner: Relational Databases
   2.3 Bigtable and Firestore: NoSQL Database Solutions
   2.4 Data Warehousing with BigQuery
3. Data Processing in GCP
   3.1 Introduction to Data Processing Pipelines
   3.2 Batch Processing with Dataflow
   3.3 Stream Processing with Pub/Sub and Dataflow
   3.4 BigQuery: Batch and Stream Processing
4. ETL and Data Transformation
   4.1 Extract, Transform, Load (ETL) Concepts
   4.2 Using Dataflow for ETL Workflows
   4.3 Automating ETL Jobs with Composer
5. Machine Learning and AI for Data Engineers
   5.1 Introduction to AI and ML on GCP
   5.2 Using BigQuery ML for Predictive Analytics
   5.3 TensorFlow and AI Platform for Data Engineers
6. Data Orchestration and Workflow Management
   6.1 Managing Workflows with Cloud Composer
   6.2 Scheduling and Monitoring Data Pipelines
   6.3 Integrating Data Services with Airflow
7. Data Integration with Google Cloud
   7.1 Integrating Data from On-Premise and Cloud Sources
   7.2 Using Cloud Data Fusion for Data Integration
   7.3 Connecting Data Pipelines with Cloud Pub/Sub
8. Data Security and Compliance
   8.1 Managing Data Access with IAM and Service Accounts
   8.2 Securing Data at Rest and in Transit(Ref: Google Cloud Platform(GCP) Essentials: Compute, Storage, and Networking)
   8.3 Compliance Standards and Best Practices
9. Data Governance and Metadata Management
   9.1 Implementing Data Lineage and Quality Checks
   9.2 Managing Metadata with Cloud Data Catalog
   9.3 Best Practices for Data Governance in GCP
10. Monitoring and Optimizing Data Pipelines
    10.1 Monitoring Pipelines with Stackdriver and Dataflow
    10.2 Cost Management and Optimization for Data Engineering
    10.3 Troubleshooting and Performance Tuning
11. Advanced Topics in Data Engineering on GCP
    11.1 Building Serverless Data Pipelines with Cloud Functions
    11.2 Leveraging Kubernetes for Data Workloads
    11.3 Implementing Real-Time Data Analytics
12. Case Studies and Industry Applications
    12.1 Data Engineering for E-Commerce
    12.2 Big Data Processing for Healthcare
    12.3 Data Solutions for Financial Services
13. Hands-On Labs and Projects
    13.1 Lab: Building a Data Pipeline with Dataflow
    13.2 Lab: Data Integration Using Cloud Data Fusion
    13.3 Lab: Creating a Real-Time Data Analytics Pipeline

Conclusion

Data Engineering with Google Cloud Platform enables participants to harness the power of GCP’s cloud services to build, scale, and optimize data pipelines. By mastering tools like BigQuery, Dataflow, and Pub/Sub, data engineers can solve complex data challenges and enable advanced analytics and machine learning capabilities. This course equips participants with the skills to create scalable, secure, and efficient data solutions in the cloud, helping businesses unlock valuable insights from their data.

Reference