Cloud Data Engineering Training in Perungudi

• Advanced Python for Data (Pandas, NumPy, APIs)
• Advanced SQL (Window functions, CTEs, Optimization)
• Relational vs. Non-Relational (NoSQL) Databases
• Linux Command Line & Bash Scripting
• Git & Version Control for Data Teams
• Data Extraction Techniques (Web Scraping & APIs)

Beginner Build an automated Python script extracting REST API data, cleaning it, and loading to PostgreSQL.

• Apache Spark Architecture (Driver, Executor, DAG)
• PySpark DataFrames and SQL API
• Data Lake Concepts (Bronze, Silver, Gold)
• Handling Parquet, Avro, and JSON formats
• Spark Performance Tuning and Partitioning
• Delta Lake and ACID Transactions in Data Lakes

Intermediate Process a multi-gigabyte raw dataset using PySpark to create an optimized Silver data lake layer.

• Workflow Orchestration with Apache Airflow
• Writing Modular DAGs (Directed Acyclic Graphs)
• Introduction to dbt for SQL transformations
• Data Modeling (Star, Snowflake, Data Vault)
• Unit Testing for Data Pipelines
• Data Lineage and Dependency Management

Intermediate Orchestrate a multi-step ETL pipeline using Airflow and dbt for in-warehouse transformations and quality tests.

• Cloud Storage (AWS S3 / Azure Data Lake)
• Serverless Warehousing (Redshift, Synapse, Snowflake)
• Cloud Data Integration (Glue / Data Factory)
• Identity and Access Management (IAM) for Data
• Cost Monitoring and Resource Optimization
• Data Sharing and Cross-Cloud Analytics

Advanced Architect and deploy a secure, end-to-end cloud data warehouse ingesting multiple sources into a unified model.

• Event-Driven Architecture Fundamentals
• Apache Kafka Core Concepts (Topics, Brokers)
• Stream Processing with Spark Structured Streaming
• Cloud Streaming (AWS Kinesis / Azure Event Hubs)
• Real-time Dashboarding (Grafana / Power BI)
• Handling Late Data and Stream Checkpointing

Advanced Build a real-time analytics pipeline ingesting live Kafka clickstream data, processing with Spark, and updating dashboards.

• Data Observability (Great Expectations, Monte Carlo)
• Data Cataloging (Apache Atlas, DataHub)
• Data Privacy, Anonymization, and GDPR/CCPA
• Role-Based Access Control (RBAC) and Encryption
• Disaster Recovery and Backup Strategies
• Automated Data Quality Alerting and SLAs

Advanced Implement a data quality framework that automatically flags anomalies, blocks bad data, and sends Slack alerts.

• Feature Engineering and Feature Stores (Feast)
• Data Versioning and Lineage (DVC, MLflow)
• Handling Training-Serving Skew
• Batch vs. Real-time Feature Computation
• Preparing Data for Model Retraining
• Model Registry and Experiment Tracking

Advanced Construct a centralized Feature Store serving consistent, low-latency features to training pipelines and real-time inference APIs.

• Parsing and Chunking Unstructured Data
• Text Embeddings and Vectorization Concepts
• Vector Databases (Pinecone, Weaviate, Milvus)
• Metadata Filtering and Hybrid Search
• Data Privacy in AI Pipelines (PII Redaction)
• Optimizing Embedding Models for Domain Data

Advanced Build an automated pipeline scraping documentation, chunking text, generating embeddings, and loading them into a Vector DB.

• CI/CD Pipelines for Data and ML (GitHub Actions)
• Model Deployment Strategies (Canary, A/B Testing)
• Monitoring Data Drift and Concept Drift
• LLM Evaluation Metrics (Faithfulness, Relevance)
• Infrastructure as Code (IaC) for ML (Terraform)
• Automated Model Retraining Triggers

Advanced Create a CI/CD pipeline automatically testing ML models for data drift and deploying them past quality gates.

• Advanced RAG Architectures (Parent-Child, Self-Query)
• GraphRAG and Knowledge Graph Integration
• Query Routing and Multi-Retrieval Strategies
• Securing LLM Inputs and Outputs (Guardrails)
• Evaluating RAG Performance (RAGAS framework)
• Caching Strategies for LLM Responses

Expert Build a secure RAG data backend with automated ingestion, intelligent chunking, and strict access controls.

• Agentic Workflows and ReAct Frameworks
• Text-to-SQL and NL2SQL Pipeline Engineering
• Tool Calling and Function Binding for LLMs
• Memory Management and State Tracking
• Preventing Hallucinations in Data Analysis
• Multi-Agent Collaboration and Routing

Expert Develop an autonomous agent connecting to a data warehouse to write complex SQL queries and generate reports.

• System Design for Scalable Data & AI Platforms
• Cost, Security, and Compliance Auditing
• Writing Technical Design Documents (TDD)
• Portfolio Presentation and Code Documentation
• Resume, LinkedIn, and Mock System Design Interviews
• Career Roadmap and Continuous Learning

Expert Deploy an MCP-powered architecture bridging an enterprise LLM with live business tools for auditable workflows.