In today’s data-driven world, organizations demand professionals who can design, manage, and optimize complex data ecosystems—both on-premise and in the cloud. This unique training program is designed to bridge the gap between traditional enterprise databases and modern cloud-native, AI-powered data engineering practices. Whether you’re working with Oracle, PostgreSQL, or Google Cloud’s managed database services (such as Cloud SQL, AlloyDB, BigQuery, or Firestore), this program will give you the tools, techniques, and mindset to thrive in modern data roles. 📘 Program Highlights 🔹 Module 1: Foundations of Data Engineering • Data pipelines: batch vs. real-time • SQL & NoSQL fundamentals • Data modeling principles • Version-controlled data with dbt & Git 🔹 Module 2: Oracle & PostgreSQL Database Administration • Architecture deep dive (Oracle 19c/23ai and PostgreSQL 16+) • Backup, recovery, and high availability concepts • Indexing, partitioning, and performance tuning • User access, auditing, and security best practices 🔹 Module 3: Google Cloud Managed Databases • Introduction to Cloud SQL (PostgreSQL / MySQL / SQL Server) • Using AlloyDB for PostgreSQL in enterprise workloads • BigQuery for analytical workloads • Firestore for serverless NoSQL architecture • IAM, VPC, Cloud Monitoring & Logging integration 🔹 Module 4: AI-Driven Data Workflows • Vector databases and embeddings (e.g., pgvector) • Integrating LLMs for data enrichment and analytics • Using Vertex AI with BigQuery • Automating anomaly detection and fraud patterns • Building intelligent data pipelines with Python + Cloud Functions 🔹 Module 5: DevOps, Monitoring, and Automation • Infrastructure as Code (Terraform for DBaaS) • CI/CD for database development • Monitoring with Prometheus, Grafana, and GCP tools • Cost optimization in hybrid environments