Data Engineering For Professionals

Training course

Overview

Data Engineering for Professionals is a comprehensive professional training course designed to develop the practical and technical capabilities required to design, build, integrate, transform, manage, and maintain modern data platforms. The course provides experienced professionals with a structured understanding of the end-to-end data engineering lifecycle, covering data ingestion, storage, modeling, transformation, orchestration, quality, security, governance, performance, and production operations. Participants develop practical skills for working with enterprise data environments while applying professional engineering principles, standards, frameworks, and best practices.

This Data Engineering for Professionals course focuses on the technologies and architectural patterns used to transform raw data into reliable and accessible information for business intelligence, analytics, artificial intelligence, and machine learning. Participants explore relational and NoSQL databases, data warehouses, data lakes, lakehouse architectures, ETL and ELT pipelines, APIs, batch processing, streaming, SQL, Python, distributed processing, and workflow orchestration. Practical exercises help participants understand how different components interact and how to select appropriate approaches based on business requirements, data volumes, performance requirements, and operational constraints.

The training emphasizes professional implementation practices, including data modeling, pipeline development, incremental processing, change data capture, testing, data quality management, metadata and lineage, observability, security, privacy, version control, CI/CD, DataOps, DevOps, cloud platforms, and performance optimization. Participants learn how to apply engineering controls throughout the data lifecycle and how to troubleshoot common technical and operational challenges. Case studies and real-world scenarios provide opportunities to analyze production problems, evaluate architectural alternatives, improve pipeline reliability, and develop maintainable and scalable data solutions.

By the end of this Data Engineering for Professionals training course, participants will be able to apply professional data engineering practices to real-world projects, design reliable data pipelines, integrate multiple data sources, build analytical data structures, optimize processing workloads, and support secure production data platforms. The course progresses from professional foundations through implementation and operational management to advanced engineering practices, enabling participants to strengthen their ability to deliver high-quality data solutions. It is particularly relevant for professionals seeking practical expertise in enterprise data engineering, cloud data platforms, analytics engineering, and modern data architecture.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Data engineers and data engineering professionals

• Database administrators and database professionals

• ETL and ELT developers

• Data integration and data migration specialists

• Data analysts and analytics professionals expanding into data engineering

• Analytics engineers and business intelligence professionals

• Software developers and application engineers working with data

• Data architects and solution architects

• Cloud engineers and cloud data professionals

• Machine learning and AI engineers working with data pipelines

• Data platform and DataOps professionals

• IT professionals responsible for enterprise data environments

• Technical specialists involved in data modernization projects

• Consultants and professionals implementing data solutions for organizations

Course Objectives

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

• Apply professional principles and practices across the data engineering lifecycle

• Analyze business and technical requirements for data engineering solutions

• Assess data sources, formats, structures, dependencies, and integration requirements

• Design scalable data engineering architectures and processing workflows

• Develop reliable ETL and ELT pipelines using SQL, Python, and appropriate engineering tools

• Apply professional data modeling techniques for operational and analytical workloads

• Implement batch, incremental, and streaming data processing approaches

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

• Apply workflow orchestration, automation, testing, logging, and error-handling practices

• Implement data quality controls, validation, reconciliation, metadata, and lineage

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

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

• Apply version control, CI/CD, DataOps, DevOps, and collaborative engineering practices

• Monitor data pipelines and diagnose production failures and performance issues

• Apply cloud data engineering concepts and modern platform practices

• Improve pipeline reliability through recovery, retry, checkpointing, and operational controls

• Apply documentation, engineering standards, code review, and maintainability practices

• Design and implement professional, scalable, secure, and production-ready data solutions

Course Content

Day 1: Professional Data Engineering Foundations, Requirements, and Architecture

Module 1: Data Engineering Principles, Architecture, Data Sources, and Professional Design

Topics

  1. Introduction to Professional Data Engineering, Responsibilities, Lifecycle, and Engineering Practices
  2. Data Engineering Requirements: Business Objectives, Analytical Use Cases, Technical Requirements, and Acceptance Criteria
  3. Data Sources and Formats: Relational, NoSQL, APIs, Files, Events, JSON, XML, and Semi-Structured Data
  4. Data Discovery and Profiling: Source Assessment, Data Structures, Volumes, Frequencies, Dependencies, and Data Characteristics
  5. Modern Data Architecture: Source, Ingestion, Storage, Processing, Serving, Consumption, and Governance Layers
  6. Databases, Data Warehouses, Data Lakes, Lakehouses, and Hybrid Data Platforms
  7. Professional Data Modeling: Entities, Relationships, Keys, Normalization, Denormalization, and Analytical Structures
  8. SQL and Python for Data Engineering: Querying, Transformation, Automation, and Reusable Engineering Components
  9. Professional Engineering Tools and Practices: Git, Documentation, Source-to-Target Mapping, Design Reviews, and Coding Standards
  10. Case Study and Practical Exercise: Analyze Requirements and Design a Professional Data Engineering Architecture

Day 2: Data Integration, ETL/ELT, Transformation, and Pipeline Development

Module 2: Professional Data Pipeline Engineering and Data Integration

Topics

  1. Data Ingestion Strategies: Batch, Incremental, Micro-Batch, Streaming, and Event-Driven Processing
  2. ETL and ELT Design Patterns, Architecture Decisions, and Implementation Practices
  3. Extracting Data from Databases, APIs, Files, Applications, and External Systems
  4. Data Transformation with SQL and Python: Filtering, Joining, Aggregation, Cleansing, Enrichment, and Standardization
  5. Incremental Data Processing, Change Data Capture, Upserts, Deduplication, and Historical Data Management
  6. Pipeline Engineering Principles: Modularity, Reusability, Parameterization, Idempotency, and Dependency Management
  7. Workflow Orchestration and Scheduling with Apache Airflow and Related Tools
  8. Pipeline Testing, Validation, Logging, Retry Mechanisms, Exception Handling, and Failure Recovery
  9. Practical Pipeline Development Tools: Source-to-Target Mapping, Data Dictionaries, Pipeline Documentation, Testing Checklists, and Version Control
  10. Real-World Exercise: Develop and Troubleshoot an Automated Multi-Source ETL/ELT Pipeline

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

Module 3: Analytical Data Engineering, Scalable Processing, and Data Quality

Topics

  1. Data Warehouse Design for Professional Analytics and Business Intelligence Workloads
  2. Dimensional Modeling: Fact Tables, Dimension Tables, Star Schemas, Snowflake Schemas, and Slowly Changing Dimensions
  3. Data Lake and Lakehouse Engineering: Storage Layers, Table Structures, Metadata, and Analytical Processing
  4. Distributed Data Processing: Parallelism, Partitioning, Shuffling, Replication, and Fault Tolerance
  5. Apache Spark for Professional Data Engineering: DataFrames, Transformations, Actions, Jobs, and Execution
  6. Batch and Streaming Processing: Architecture, Workloads, Latency, Throughput, and Processing Requirements
  7. Data Quality Engineering: Accuracy, Completeness, Consistency, Validity, Uniqueness, and Timeliness
  8. Data Validation, Reconciliation, Profiling, Automated Quality Rules, and Quality Monitoring
  9. Metadata, Data Lineage, Data Cataloging, Business Definitions, and Professional Documentation
  10. Case Study and Practical Exercise: Build and Validate a Reliable Analytical Data Pipeline from Raw Data to Trusted Dataset

Day 4: Performance, Security, Cloud, and Engineering Operations

Module 4: Professional Data Platform Optimization, Security, Automation, and Operations

Topics

  1. Data Engineering Performance Management: Query Optimization, Execution Plans, Partitioning, Indexing, and Efficient Transformations
  2. Pipeline Scalability and Capacity Planning: Workload Management, Parallelism, Resource Allocation, and Processing Efficiency
  3. Data Storage Optimization: Compression, File Formats, Partition Strategies, Clustering, Caching, and Storage Lifecycle
  4. Cloud Data Engineering Architecture: Compute, Storage, Networking, Managed Services, and Elastic Workloads
  5. Data Platform Security: Authentication, Authorization, Role-Based Access, Encryption, Secrets Management, and Network Controls
  6. Data Privacy and Protection: Sensitive Data Classification, Masking, Tokenization, Retention, and Controlled Access
  7. Data Engineering Testing and CI/CD: Unit Testing, Integration Testing, Data Validation, Automated Deployment, and Release Controls
  8. DataOps and DevOps Practices: Collaboration, Version Control, Code Review, Automation, Reproducibility, and Continuous Improvement
  9. Monitoring and Observability: Pipeline Health, Data Freshness, Latency, Throughput, Logs, Alerts, Lineage, and Incident Management
  10. Real-World Scenario: Diagnose a Production Data Pipeline Failure and Implement Performance, Security, and Reliability Improvements

Day 5: Production Engineering, Governance, Modernization, and Professional Capstone

Module 5: Production Data Engineering, Reliability, Governance, and Advanced Implementation

Topics

  1. Production-Ready Data Engineering: Reliability, Availability, Maintainability, Scalability, and Operational Readiness
  2. Pipeline Reliability Engineering: Idempotency, Checkpointing, Retry Strategies, Backfills, Replay, Recovery, and Disaster Recovery
  3. Advanced Data Governance: Data Ownership, Stewardship, Policies, Standards, Controls, Metadata, and Accountability
  4. Data Contracts, Schema Evolution, Interface Management, Compatibility, and Reliable Data Exchange
  5. Advanced Data Observability and Engineering KPIs: Freshness, Quality, Latency, Throughput, Failure Rates, and Service Objectives
  6. Data Platform Modernization: Legacy ETL Migration, Cloud Migration, Architecture Refactoring, and Technology Transformation
  7. Engineering Maintainability: Technical Debt, Reusability, Documentation, Refactoring, Code Standards, and Lifecycle Management
  8. Professional Data Engineering Best Practices: Architecture Reviews, Code Reviews, Testing Standards, Security-by-Design, and Continuous Improvement
  9. Comprehensive Case Study: Design and Evaluate an Enterprise Data Engineering Solution from Requirements through Production Operations
  10. Professional Capstone Exercise: Design, Develop, Test, Secure, Optimize, Document, Monitor, and Present an End-to-End Production-Ready Data Engineering Solution

 

Course Schedules:

Dates Fees Location Apply