– Overview of Google Cloud Platform (GCP)
– GCP infrastructure and core services
– Introduction to data engineering concepts
– Key GCP tools and services for data engineering
– Setting up a GCP environment
– GCP console and CLI tools
– Configuring Cloud Storage and Identity & Access Management (IAM)

– Introduction to Apache Beam and Dataflow
– What is Apache Beam?
– Understanding dataflow concepts (batch and stream processing)
– Building data pipelines using Dataflow
– Preparing a Dataflow pipeline (input/output, transformations)
– Working with Dataflow SDKs (Java, Python)
– Creating batch and real-time pipelines
– Monitoring and debugging Dataflow jobs
– Hands-on Project: Building a simple Dataflow pipeline for data transformation

– Introduction to BigQuery
– Overview of BigQuery and its architecture
– BigQuery data types, tables, and datasets
– Writing SQL queries in BigQuery
– Basic SQL queries (SELECT, JOIN, WHERE, GROUP BY, etc.)
– Advanced BigQuery features (Window Functions, Subqueries, UDFs)
– Optimizing BigQuery performance
– Query optimization techniques
– Partitioning and clustering data
– Using materialized views
– Hands-on Project: Analyzing large datasets with BigQuery SQL queries

– Introduction to Cloud Pub/Sub
– Understanding the publish/subscribe messaging pattern
– Setting up and configuring Cloud Pub/Sub topics and subscriptions
– Streaming data with Cloud Pub/Sub
– Connecting Cloud Pub/Sub with Dataflow for real-time streaming
– Handling event-driven data and triggers
– Best practices for designing scalable, fault-tolerant Pub/Sub systems
– Hands-on Project: Building a real-time data pipeline with Cloud Pub/Sub and Dataflow

– Introduction to AI Platform and its services
– AI Platform’s tools for model training, prediction, and deployment
– Preparing data for machine learning in the cloud
– Building machine learning models with AI Platform
– Training models with TensorFlow on AI Platform
– Using AutoML for specific tasks (e.g., vision, language processing)
– Hyperparameter tuning and optimization
– Model deployment and monitoring on AI Platform
– Serving models in the cloud for real-time predictions
– Managing models in production
– Hands-on Project: Building and deploying a machine learning model using AI Platform

– Overview of data security in the cloud
– Understanding the shared responsibility model
– Security considerations for data engineering tasks
– Securing data with IAM and encryption
– Managing access control using IAM roles and policies
– Encrypting data at rest and in transit
– Compliance and governance in GCP
– Managing and auditing data with Cloud Audit Logs
– Implementing data governance policies using Cloud Data Loss Prevention (DLP)
– Ensuring compliance with GDPR, HIPAA, and other regulations
– Best practices for secure data handling in GCP
– Hands-on Project: Implementing IAM and encryption for a data pipeline

– Final Project: End-to-End Data Pipeline
– Design a comprehensive data pipeline using Dataflow, Cloud Pub/Sub, BigQuery, and AI Platform
– Implement real-time streaming, batch processing, and machine learning
– Ensure proper data security and governance
– Review of key concepts and tools covered
– Troubleshooting common challenges in data engineering on GCP
– Final assessment and course wrap-up

– Quizzes: Short quizzes after each module to reinforce key concepts
– Hands-on Projects: Projects to develop practical experience with tools like Dataflow, BigQuery, Cloud Pub/Sub, and AI Platform
– Final Project: An end-to-end solution covering data ingestion, processing, and analytics using GCP tools