Advanced Data Engineering

Training course

Overview

Advanced Data Engineering is a professional-level discipline focused on designing, implementing, optimizing, and governing sophisticated data platforms capable of supporting enterprise analytics, artificial intelligence, machine learning, real-time applications, and high-volume data workloads. This comprehensive Advanced Data Engineering training course builds upon core data engineering principles and develops advanced capabilities in distributed data processing, scalable pipeline architecture, cloud-native platforms, streaming systems, data orchestration, reliability engineering, and production operations. Participants gain practical knowledge required to engineer robust data solutions across complex and rapidly changing technology environments.

This Advanced Data Engineering course explores advanced data architecture patterns, sophisticated ETL and ELT strategies, distributed computing, data lakehouse architectures, real-time data processing, event-driven systems, and modern analytical platforms. Participants learn how to engineer high-performance pipelines using technologies and concepts such as SQL, Python, Apache Spark, workflow orchestration, event streaming, cloud data platforms, and modern table and storage formats. The course emphasizes architectural decision-making, scalability, parallel processing, fault tolerance, workload management, and efficient resource utilization for large and complex datasets.

The training also addresses advanced engineering practices for data quality, observability, governance, security, privacy, DevOps, DataOps, CI/CD, infrastructure automation, testing, and production reliability. Participants learn advanced approaches to pipeline monitoring, lineage, metadata management, data contracts, schema evolution, incident response, disaster recovery, performance optimization, and cloud cost management. Practical case studies and engineering exercises enable participants to evaluate architectural alternatives, diagnose production problems, optimize data workloads, and apply recognized engineering and operational practices to real-world data platforms.

By the end of this Advanced Data Engineering training course, participants will be able to design and operate scalable, secure, observable, resilient, and production-ready data engineering platforms. They will be equipped to address complex data integration challenges, optimize distributed processing workloads, implement batch and streaming architectures, modernize legacy data platforms, and establish engineering practices that support long-term platform reliability and maintainability. The course provides a structured progression from advanced foundations to enterprise-scale implementation, making it valuable for experienced data engineers, architects, developers, cloud professionals, and technical leaders responsible for sophisticated data environments.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Experienced data engineers and senior data engineers

• Data architects and enterprise architects

• Data platform engineers and cloud data engineers

• Database administrators and senior database professionals

• ETL/ELT developers and data integration specialists

• Software engineers working with large-scale data systems

• Analytics engineers and business intelligence engineering professionals

• Machine learning and AI engineers working with production data

• Cloud architects and infrastructure engineers supporting data platforms

• DataOps, DevOps, and platform engineering professionals

• Technical leads and engineering managers responsible for data platforms

• Professionals responsible for data modernization and cloud migration

• Senior IT professionals involved in enterprise data architecture and transformation

• Consultants and technical specialists implementing advanced data solutions

Course Objectives

By the end of the training, participants will be able to:

• Apply advanced principles and practices across the modern data engineering lifecycle

• Design scalable enterprise data engineering architectures for complex workloads

• Evaluate data warehouse, data lake, lakehouse, hybrid, and event-driven architectures

• Engineer advanced batch, incremental, micro-batch, and streaming data pipelines

• Apply distributed processing concepts using technologies such as Apache Spark

• Optimize large-scale SQL, data transformation, storage, and compute workloads

• Design resilient pipelines using fault tolerance, checkpointing, idempotency, and recovery patterns

• Implement advanced data quality, validation, observability, metadata, and lineage practices

• Apply data contracts, schema management, schema evolution, and reliable integration patterns

• Design secure data platforms using advanced authentication, authorization, encryption, and secrets-management practices

• Implement CI/CD, automated testing, infrastructure automation, DataOps, and DevOps practices

• Manage cloud scalability, capacity, workload performance, and data platform costs

• Design event-driven and real-time data architectures for low-latency applications

• Diagnose and resolve complex production data pipeline and platform failures

• Apply advanced governance, privacy, compliance, and lifecycle management principles

• Modernize legacy data platforms and migrate complex workloads to modern architectures

• Establish engineering standards, operational controls, and continuous improvement practices

• Develop and present enterprise-scale, production-ready data engineering solutions

Course Content

Day 1: Advanced Data Architecture, Distributed Systems, and Engineering Design

Module 1: Advanced Data Engineering Architecture and Scalable System Design

Topics

  1. Advanced Data Engineering Principles, Lifecycle, Responsibilities, and Engineering Standards
  2. Enterprise Data Architecture Patterns: Warehouse, Lake, Lakehouse, Mesh, Fabric, and Hybrid Approaches
  3. Advanced Data Platform Design: Ingestion, Storage, Processing, Serving, Consumption, and Control Layers
  4. Distributed Data Systems: Parallelism, Partitioning, Replication, Sharding, Fault Tolerance, and Scalability
  5. Advanced Data Modeling for Analytical, Operational, Event-Based, and Machine Learning Workloads
  6. Data Contracts, Schema Management, Schema Evolution, Compatibility, and Interface Design
  7. Advanced ETL and ELT Architecture: Incremental Processing, Change Data Capture, and Reprocessing
  8. Architecture Decision Records, Design Trade-Offs, Capacity Planning, and Technology Selection
  9. Practical Architecture Tools: Data Flow Diagrams, Source-to-Target Mapping, Data Lineage, Design Reviews, and Engineering Checklists
  10. Case Study and Architecture Exercise: Designing a Scalable Enterprise Data Platform for a Complex Multi-Source Environment

Day 2: Advanced Data Pipelines, Distributed Processing, and Streaming

Module 2: High-Performance Data Processing and Real-Time Engineering

Topics

  1. Advanced Pipeline Engineering: Dependency Management, Idempotency, Reusability, Parameterization, and Backfills
  2. Advanced Apache Spark Architecture: Drivers, Executors, Jobs, Stages, Tasks, and Distributed Execution
  3. Spark DataFrames, Transformations, Actions, Joins, Aggregations, Partitioning, and Optimization
  4. Advanced Distributed Processing: Shuffles, Data Skew, Broadcast Operations, Caching, and Resource Management
  5. Advanced Batch Processing: Incremental Loads, Windowing, Historical Processing, and Large-Scale Transformations
  6. Event-Driven Data Engineering and Streaming Architecture Fundamentals
  7. Streaming Platforms, Topics, Partitions, Consumer Groups, Delivery Semantics, and Event Processing
  8. Real-Time Data Pipelines: Windowing, State Management, Checkpointing, Late Data, and Exactly-Once Processing Concepts
  9. Advanced Workflow Orchestration with Apache Airflow and Related Pipeline Automation Practices
  10. Practical Exercise and Real-World Scenario: Engineering a High-Volume Batch and Streaming Data Pipeline

Day 3: Advanced Data Quality, Observability, Governance, and Security

Module 3: Trusted, Observable, Secure, and Governed Data Platforms

Topics

  1. Advanced Data Quality Engineering: Quality Dimensions, Automated Controls, Thresholds, and Quality SLAs
  2. Data Validation, Reconciliation, Anomaly Detection, Drift Detection, and Automated Data Testing
  3. Data Observability: Freshness, Volume, Distribution, Lineage, Performance, and Pipeline Health Monitoring
  4. Metadata Management, Data Cataloging, Technical Metadata, Business Metadata, and Automated Lineage
  5. Advanced Data Governance: Ownership, Stewardship, Policies, Standards, Controls, and Accountability
  6. Data Security Architecture: Identity, Access Management, Encryption, Key Management, Secrets, and Network Security
  7. Privacy Engineering: Sensitive Data Classification, Masking, Tokenization, Retention, and Controlled Access
  8. Compliance-Oriented Data Engineering, Auditability, Traceability, Evidence Management, and Control Frameworks
  9. Incident Management, Root-Cause Analysis, Data Reliability Engineering, and Production Troubleshooting
  10. Case Study and Practical Exercise: Diagnosing a Data Quality and Security Incident Across an Enterprise Data Platform

Day 4: Cloud-Native Data Engineering, Performance Optimization, and DevOps

Module 4: Advanced Cloud Platforms, Automation, Performance, and Operational Engineering

Topics

  1. Cloud-Native Data Engineering Architecture: Compute, Storage, Networking, Security, and Managed Services
  2. Advanced Cloud Data Warehouses, Data Lakes, Lakehouses, and Elastic Processing Architectures
  3. Performance Engineering: Query Optimization, Partitioning, Clustering, Indexing, Caching, and Storage Optimization
  4. Distributed Workload Optimization: Resource Allocation, Parallelism, Memory Management, and Execution Planning
  5. Scalability and Elasticity: Autoscaling, Capacity Management, Workload Isolation, and Resource Governance
  6. Cloud Data Platform Cost Optimization, FinOps Principles, Usage Monitoring, and Cost Allocation
  7. Data Engineering CI/CD: Source Control, Automated Testing, Deployment Pipelines, Release Management, and Rollback
  8. Infrastructure as Code, Environment Management, Configuration Automation, and Reproducible Data Platforms
  9. Advanced DataOps and DevOps Practices: Collaboration, Automation, Monitoring, Quality Gates, and Continuous Delivery
  10. Real-World Scenario: Optimizing and Modernizing a High-Cost, Low-Performance Cloud Data Engineering Environment

Day 5: Enterprise Reliability, Modernization, and Advanced Data Engineering Capstone

Module 5: Enterprise Data Engineering Strategy, Reliability, Modernization, and Capstone Implementation

Topics

  1. Production-Grade Data Engineering: Reliability, Availability, Maintainability, Scalability, and Service Objectives
  2. Advanced Resilience Engineering: Disaster Recovery, Failover, Checkpointing, Recovery, Replay, and Business Continuity
  3. Advanced Data Platform Lifecycle Management, Technical Debt, Refactoring, Reusability, and Maintainability
  4. Legacy Data Engineering Modernization: ETL Transformation, Platform Migration, Cloud Migration, and Architecture Renewal
  5. Advanced Data Architecture for AI, Machine Learning, Real-Time Analytics, and Intelligent Applications
  6. Enterprise Data Platform Governance, Engineering Standards, Architecture Reviews, and Operating Models
  7. Advanced Data Engineering KPIs and SLOs: Pipeline Reliability, Latency, Freshness, Throughput, Cost, and Quality
  8. Strategic Technology Evaluation: Open-Source Platforms, Managed Services, Vendor Selection, and Architecture Trade-Offs
  9. Comprehensive Enterprise Case Study: Designing a Secure, Scalable, Observable, and Resilient Modern Data Platform
  10. Advanced Capstone Exercise: Design, Engineer, Test, Optimize, Govern, Monitor, and Present a Production-Ready Enterprise Data Engineering Solution

 

Course Schedules:

Dates Fees Location Apply