Data Engineering

Training course

Overview

Data Engineering is a professional discipline focused on designing, building, integrating, processing, and maintaining reliable data systems that support analytics, reporting, artificial intelligence, machine learning, and operational decision-making. This comprehensive Data Engineering training course provides participants with practical knowledge of the complete data engineering lifecycle, from data ingestion and storage to transformation, orchestration, quality management, governance, and production operations. The course introduces modern data engineering principles, architectures, tools, and best practices used to create scalable and dependable data platforms.

This Data Engineering course develops a strong foundation in data architecture, databases, data modeling, batch and streaming pipelines, ETL and ELT, data integration, distributed processing, and cloud-based data platforms. Participants explore how structured, semi-structured, and unstructured data can be collected and transformed into trusted datasets for business intelligence, analytics, and machine learning. Practical exercises and real-world scenarios help participants understand how technologies such as SQL, Python, APIs, workflow orchestration platforms, distributed processing frameworks, data warehouses, data lakes, and lakehouse architectures work together within modern data environments.

The training also addresses advanced data engineering practices including pipeline automation, orchestration, data quality, metadata management, observability, performance optimization, security, privacy, governance, CI/CD, infrastructure considerations, and cloud data engineering. Participants learn how to design resilient and maintainable data pipelines while applying recognized engineering practices and relevant frameworks such as ETL/ELT patterns, dimensional modeling, DataOps principles, DevOps practices, data governance principles, and security-by-design approaches. Case studies and practical implementation exercises provide opportunities to analyze common data engineering challenges and develop appropriate technical solutions.

By the end of this Data Engineering training course, participants will be able to design and implement end-to-end data pipelines, manage diverse data sources, transform and validate data, automate workflows, optimize data processing, and support reliable production data platforms. The course is suitable for professionals seeking to strengthen their practical data engineering capabilities as well as organizations developing modern data platforms for analytics and AI. Through a structured progression from foundational concepts to advanced engineering practices, participants gain the technical, operational, and problem-solving skills required to contribute effectively to modern data engineering projects.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Data engineers and aspiring data engineers

• Database administrators and database professionals

• Data analysts and analytics professionals transitioning into data engineering

• Business intelligence and reporting professionals

• Software developers and application engineers working with data platforms

• Data architects and solution architects

• Cloud engineers and infrastructure professionals supporting data workloads

• Machine learning and AI engineers working with production data pipelines

• ETL developers and data integration specialists

• Data platform and data operations professionals

• IT professionals responsible for enterprise data systems

• Technical project managers and technology team leaders

• Professionals involved in data modernization and digital transformation initiatives

Course Objectives

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

• Explain the principles, responsibilities, architecture, and lifecycle of modern data engineering

• Identify and assess structured, semi-structured, and unstructured data sources

• Design appropriate data ingestion, integration, storage, and processing architectures

• Apply SQL and Python techniques to develop practical data engineering solutions

• Design and implement reliable ETL and ELT pipelines

• Apply appropriate data modeling techniques for analytical and operational workloads

• Work with data warehouses, data lakes, and modern lakehouse architectures

• Build and orchestrate automated batch and streaming data pipelines

• Apply distributed data processing concepts to large-scale datasets

• Implement data validation, quality controls, monitoring, and observability practices

• Optimize data pipelines, queries, storage, and processing workloads

• Apply data security, privacy, governance, and access-control principles

• Implement engineering practices including version control, testing, CI/CD, and DataOps

• Evaluate cloud-based data engineering architectures and services

• Troubleshoot pipeline failures, performance issues, data quality problems, and operational incidents

• Design maintainable, scalable, secure, and production-ready data engineering solutions

Course Content

Day 1: Foundations of Data Engineering and Data Platform Architecture

Module 1: Data Engineering Fundamentals, Architecture, and Data Lifecycle

Topics

  1. Introduction to Data Engineering and the Modern Data Engineering Lifecycle
  2. Roles, Responsibilities, Skills, and Professional Practices of Data Engineers
  3. Data Sources and Data Types: Structured, Semi-Structured, and Unstructured Data
  4. Databases, Files, APIs, Applications, Events, and External Data Sources
  5. Data Engineering Architecture: Sources, Ingestion, Storage, Processing, Serving, and Consumption Layers
  6. Relational Databases, NoSQL Databases, Data Warehouses, Data Lakes, and Lakehouse Platforms
  7. Data Modeling Fundamentals: Entities, Relationships, Keys, Schemas, Normalization, and Denormalization
  8. SQL and Python Foundations for Practical Data Engineering
  9. Data Engineering Tools, Development Environments, Version Control, Documentation, and Best Practices
  10. Case Study and Practical Exercise: Designing an End-to-End Data Platform for a Business Scenario

Day 2: Data Ingestion, Integration, Transformation, and Pipeline Development

Module 2: ETL, ELT, Data Integration, and Pipeline Engineering

Topics

  1. Data Ingestion Strategies: Batch, Incremental, Micro-Batch, and Event-Driven Ingestion
  2. ETL and ELT Architecture, Processing Patterns, and Use Cases
  3. Extracting Data from Databases, Files, APIs, Applications, and Cloud Sources
  4. Data Transformation with SQL and Python: Cleaning, Filtering, Joining, Aggregation, and Enrichment
  5. Incremental Loading, Change Data Capture, Upserts, Deduplication, and Historical Data Processing
  6. Data Pipeline Design: Dependencies, Parameters, Reusability, Idempotency, and Error Handling
  7. Workflow Orchestration and Scheduling with Tools such as Apache Airflow
  8. Pipeline Testing, Validation, Logging, Retry Strategies, and Failure Recovery
  9. Practical Data Integration Tools, Source-to-Target Mapping, Pipeline Documentation, and Engineering Checklists
  10. Real-World Exercise: Building and Troubleshooting an Automated ETL/ELT Data Pipeline

Day 3: Data Warehousing, Distributed Processing, Streaming, and Data Quality

Module 3: Scalable Data Processing and Trusted Analytical Data

Topics

  1. Data Warehouse Architecture and Analytical Data Processing
  2. Dimensional Modeling: Fact Tables, Dimension Tables, Star Schemas, and Snowflake Schemas
  3. Data Lakes and Lakehouse Architecture: Storage, Table Formats, Metadata, and Analytical Workloads
  4. Distributed Data Processing Concepts: Partitioning, Parallelism, Shuffling, and Fault Tolerance
  5. Apache Spark Fundamentals and Large-Scale Data Processing with DataFrames
  6. Batch Processing Versus Real-Time and Streaming Data Engineering
  7. Event Streaming Concepts, Message Queues, Topics, Partitions, and Consumer Processing
  8. Data Quality Engineering: Accuracy, Completeness, Consistency, Validity, Timeliness, and Uniqueness
  9. Data Validation, Profiling, Reconciliation, Quality Rules, and Data Quality Monitoring
  10. Case Study and Practical Exercise: Designing a Scalable Data Pipeline for High-Volume Analytical Data

Day 4: Advanced Data Engineering, Cloud, Security, and Operational Reliability

Module 4: Cloud Data Engineering, Automation, Security, and Performance Optimization

Topics

  1. Cloud Data Engineering Architecture and Core Cloud Service Models
  2. Designing Scalable Cloud Data Pipelines, Storage, Compute, and Processing Layers
  3. Cloud Data Warehouses, Data Lakes, Lakehouses, and Hybrid Data Architectures
  4. Pipeline Performance Optimization: Query Tuning, Partitioning, Indexing, Caching, and Efficient Data Processing
  5. Scalability, Capacity Planning, Cost Optimization, and Workload Management
  6. Data Pipeline Security: Authentication, Authorization, Encryption, Secrets Management, and Network Controls
  7. Data Privacy, Sensitive Data Protection, Data Masking, Access Policies, and Governance Principles
  8. Data Engineering Testing, CI/CD, Infrastructure Automation, Version Control, and DataOps Practices
  9. Data Observability, Monitoring, Metadata, Lineage, Alerting, Incident Management, and Production Support
  10. Real-World Scenario: Diagnosing a Failed Production Pipeline and Designing a Secure, Resilient, and Optimized Solution

Day 5: Production Data Engineering, Governance, Advanced Architecture, and Capstone

Module 5: Enterprise Data Engineering, Reliability, Optimization, and Strategic Implementation

Topics

  1. Production-Ready Data Pipeline Architecture and Engineering Standards
  2. Advanced Pipeline Reliability: Idempotency, Checkpointing, Recovery, Backfills, and Disaster Recovery
  3. Advanced Data Governance, Metadata Management, Data Lineage, Cataloging, and Data Ownership
  4. Data Platform Observability and Engineering KPIs: Reliability, Freshness, Latency, Throughput, and Failure Rates
  5. Advanced Data Architecture Patterns for Analytics, AI, Machine Learning, and Real-Time Applications
  6. Data Engineering Modernization: Legacy ETL Migration, Cloud Migration, and Platform Transformation
  7. Managing Technical Debt, Maintainability, Reusability, Documentation, and Data Engineering Lifecycle Management
  8. Enterprise Best Practices for Collaboration, Code Review, CI/CD, DataOps, DevOps, and Continuous Improvement
  9. Comprehensive Case Study: Designing an Enterprise-Scale Data Engineering Platform from Requirements to Production
  10. Capstone Exercise: Develop, Document, Validate, Secure, Monitor, and Present an End-to-End Production-Ready Data Engineering Solution

 

Course Schedules:

Dates Fees Location Apply