Practical Data Engineering

Training course

Overview

Practical Data Engineering is a hands-on professional training course designed to develop the practical skills required to collect, integrate, transform, validate, store, and manage data for analytics, reporting, artificial intelligence, and operational decision-making. The course focuses on applying data engineering concepts through realistic workflows, practical tools, structured exercises, and end-to-end implementation activities. Participants learn how to work with real-world data sources and build reliable data pipelines while applying professional engineering practices throughout the data lifecycle.

This Practical Data Engineering course covers the essential technologies and techniques used in modern data engineering, including SQL, Python, databases, APIs, files, ETL and ELT, data modeling, data warehouses, data lakes, lakehouse concepts, workflow orchestration, batch processing, and data quality management. Participants progressively develop practical solutions by extracting data from multiple sources, transforming and validating datasets, loading information into analytical structures, and automating repeatable workflows. The training emphasizes practical problem-solving rather than theoretical concepts alone.

The course also develops practical capabilities in pipeline testing, error handling, monitoring, logging, performance optimization, security, documentation, version control, cloud data platforms, and operational support. Participants work with practical tools such as SQL development environments, Python data-processing libraries, Git, workflow orchestration platforms, data profiling techniques, pipeline monitoring dashboards, source-to-target mappings, validation checklists, and operational runbooks. Case studies and troubleshooting exercises simulate common production situations, enabling participants to identify failures, investigate root causes, apply corrective actions, and improve pipeline reliability.

By the end of this Practical Data Engineering training course, participants will be able to design and implement practical end-to-end data engineering solutions, automate data workflows, validate data quality, troubleshoot pipeline problems, optimize processing, and prepare data for analytical and operational use. The course progresses from foundational implementation through increasingly complex integration, processing, quality, performance, and production scenarios. Participants gain practical experience that can be applied directly to organizational data engineering projects and modern data platform environments.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Aspiring data engineers and junior data engineers

• Data engineers seeking stronger hands-on implementation skills

• Data analysts and analytics professionals moving into data engineering

• ETL and data integration professionals

• Database administrators and database professionals

• Business intelligence and reporting professionals

• Software developers working with databases and data pipelines

• Analytics engineers and data platform professionals

• Cloud and IT professionals supporting data workloads

• Machine learning and AI professionals working with data preparation pipelines

• Data quality and data operations professionals

• Technical professionals responsible for data migration and integration

• IT professionals seeking practical data engineering skills

• Consultants and technical specialists implementing data solutions

Course Objectives

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

• Apply practical data engineering principles throughout the data lifecycle

• Identify, connect to, and assess common structured and semi-structured data sources

• Use SQL and Python to extract, transform, validate, and prepare data

• Design practical data ingestion and integration workflows

• Build ETL and ELT pipelines using appropriate tools and techniques

• Apply data modeling principles to create useful analytical data structures

• Work with databases, data warehouses, data lakes, and modern analytical platforms

• Implement batch, incremental, and basic streaming data processing workflows

• Automate data pipelines using workflow orchestration and scheduling tools

• Apply practical data quality checks, validation, reconciliation, and error handling

• Implement logging, monitoring, alerting, and pipeline troubleshooting procedures

• Optimize queries, transformations, storage structures, and pipeline execution

• Apply practical data security, access control, privacy, and governance practices

• Use Git and collaborative engineering practices for data pipeline development

• Apply testing, documentation, deployment, and operational support practices

• Work with cloud-based data engineering concepts and services

• Troubleshoot realistic data pipeline failures and production issues

• Develop and present an end-to-end practical data engineering solution

Course Content

Day 1: Data Engineering Fundamentals, Data Sources, SQL, Python, and Initial Pipeline Development

Module 1: Practical Data Engineering Foundations and Data Preparation

Topics

  1. Introduction to Practical Data Engineering, Tools, Workflows, and End-to-End Data Lifecycle
  2. Identifying Business Data Requirements, Analytical Use Cases, Inputs, Outputs, and Success Criteria
  3. Working with Data Sources: Relational Databases, CSV Files, JSON, APIs, Applications, and External Data
  4. Practical Data Discovery and Profiling: Structure, Types, Volumes, Missing Values, Duplicates, and Data Patterns
  5. SQL for Data Engineering: Queries, Joins, Aggregations, Subqueries, Common Table Expressions, and Data Validation
  6. Python for Data Engineering: Data Structures, Functions, File Handling, APIs, Libraries, and Automation
  7. Data Cleaning and Transformation: Standardization, Filtering, Deduplication, Type Conversion, and Data Enrichment
  8. Practical Data Modeling: Tables, Keys, Relationships, Normalization, Denormalization, and Analytical Structures
  9. Practical Engineering Tools: Git, Development Environments, Data Dictionaries, Source-to-Target Mappings, and Documentation
  10. Hands-On Exercise: Extract, Profile, Clean, Transform, and Document a Multi-Source Dataset

Day 2: ETL/ELT, Data Integration, Pipelines, and Workflow Automation

Module 2: Practical Data Pipeline Development and Orchestration

Topics

  1. Practical ETL and ELT Architecture: Extract, Transform, Load, Transformation Layers, and Processing Workflows
  2. Building Data Ingestion Processes from Databases, Files, APIs, and Application Systems
  3. Data Transformation with SQL and Python: Joins, Aggregations, Business Rules, Enrichment, and Standardization
  4. Incremental Data Loading, Upserts, Change Data Capture, Deduplication, and Historical Data Processing
  5. Designing Reliable Pipelines: Dependencies, Parameters, Modularity, Reusability, Idempotency, and Scheduling
  6. Loading Data into Data Warehouses, Data Lakes, and Analytical Storage Structures
  7. Workflow Orchestration with Apache Airflow: DAGs, Tasks, Operators, Scheduling, Dependencies, and Monitoring
  8. Pipeline Error Handling: Retries, Logging, Exceptions, Validation Failures, Recovery, and Reprocessing
  9. Practical Pipeline Development Tools: Configuration Files, Environment Variables, Runbooks, Checklists, and Version Control
  10. Hands-On Case Study: Build and Automate an End-to-End ETL/ELT Pipeline from Multiple Data Sources

Day 3: Data Warehousing, Quality, Testing, and Scalable Processing

Module 3: Practical Analytical Data Engineering and Data Quality Management

Topics

  1. Building Practical Data Warehouse Structures for Reporting and Business Intelligence
  2. Dimensional Modeling: Fact Tables, Dimension Tables, Star Schemas, Snowflake Schemas, and Historical Data
  3. Data Lake and Lakehouse Concepts: File Storage, Table Structures, Metadata, and Analytical Workloads
  4. Practical Batch Processing: Scheduling, Large-Volume Transformations, Incremental Processing, and Backfills
  5. Introduction to Distributed Processing with Apache Spark and DataFrame-Based Data Transformation
  6. Data Quality Implementation: Accuracy, Completeness, Consistency, Validity, Uniqueness, and Timeliness
  7. Data Validation and Reconciliation: Business Rules, Record Counts, Control Totals, Referential Checks, and Exception Reports
  8. Pipeline Testing: Unit Testing, Integration Testing, Data Testing, Regression Testing, and Acceptance Checks
  9. Practical Quality and Testing Tools: Validation Scripts, Quality Dashboards, Test Cases, Defect Logs, and Data Profiling
  10. Real-World Exercise: Build, Test, Validate, and Troubleshoot a Practical Analytical Data Pipeline

Day 4: Performance, Security, Cloud, Monitoring, and Production Support

Module 4: Practical Data Platform Optimization, Security, and Operations

Topics

  1. Practical Pipeline Performance Optimization: Query Tuning, Efficient Transformations, Partitioning, Indexing, and Caching
  2. Storage and File Optimization: Compression, File Formats, Partitioning, Clustering, and Efficient Data Layout
  3. Pipeline Scalability and Resource Management: Workload Size, Parallel Processing, Capacity, and Execution Efficiency
  4. Cloud Data Engineering Fundamentals: Cloud Storage, Compute, Databases, Warehouses, Lakes, and Managed Services
  5. Practical Data Security: Authentication, Authorization, Access Control, Encryption, Secrets, and Secure Connections
  6. Data Privacy and Protection: Sensitive Data Identification, Masking, Retention, Controlled Access, and Safe Data Handling
  7. Pipeline Monitoring and Observability: Logs, Metrics, Alerts, Data Freshness, Latency, Throughput, and Pipeline Health
  8. Production Troubleshooting: Failure Detection, Log Analysis, Root-Cause Investigation, Recovery, and Incident Documentation
  9. Practical Operational Tools: Monitoring Dashboards, Alert Rules, Runbooks, Incident Logs, Service Checklists, and Deployment Records
  10. Real-World Scenario: Diagnose, Repair, Optimize, Secure, and Monitor a Failing Production Data Pipeline

Day 5: Advanced Practical Implementation, Reliability, Automation, and Capstone

Module 5: End-to-End Data Engineering Implementation and Production Readiness

Topics

  1. Production-Ready Data Engineering: Reliability, Maintainability, Scalability, Reproducibility, and Operational Readiness
  2. Advanced Pipeline Reliability: Idempotency, Checkpointing, Retry Strategies, Recovery, Replay, and Backfills
  3. Advanced Data Integration: APIs, Change Data Capture, Event-Based Data, Schema Evolution, and Complex Dependencies
  4. Advanced Data Observability: Pipeline Monitoring, Data Lineage, Freshness, Quality Metrics, Alerts, and Operational Dashboards
  5. CI/CD for Data Engineering: Git Workflows, Automated Testing, Code Review, Deployment Automation, and Rollback
  6. Practical DataOps Practices: Collaboration, Automation, Quality Gates, Documentation, Monitoring, and Continuous Improvement
  7. Data Governance in Practice: Metadata, Data Ownership, Lineage, Access Policies, Data Standards, and Auditability
  8. Data Engineering Modernization: Improving Legacy Pipelines, Cloud Migration, Refactoring, Technical Debt, and Platform Improvement
  9. Comprehensive Case Study: Design and Implement a Secure, Scalable, Observable, and Maintainable Data Engineering Platform
  10. Practical Capstone Exercise: Extract, Integrate, Transform, Validate, Load, Automate, Test, Monitor, Secure, Document, and Present an End-to-End Data Engineering Solution

 

Course Schedules:

Dates Fees Location Apply