Data Engineering Strategy 2 Days Training in Chicago, IL
About this Event
Duration:
2 Days (20 Executive Education Credits)
• 16 from the live training session (8 Credits per day)
• 2 from Pre-Training Workbook (data readiness assessment & architecture reflection)
• 2 from Post-Training Resource Pack (implementation activities & data engineering roadmap)
Mode:
Instructor-Led Virtual Live Session / In-Person / Corporate In-House
Course Overview
Modern Data Engineering Essentials is a comprehensive and practical program designed to help professionals understand how modern organizations collect, store, process, integrate, and manage data at scale. As businesses increasingly depend on data-driven strategies, the ability to build reliable, scalable, and efficient data ecosystems has become a critical organizational capability.
This program introduces participants to the foundations of modern data engineering, including data architecture, data pipelines, data integration, cloud data platforms, data lakes, data warehouses, data quality management, governance frameworks, and real-time data processing concepts.
Participants will learn how data moves through modern organizations—from source systems to business intelligence platforms—and how effective data engineering supports analytics, Artiflcial Intelligence, Machine Learning, reporting, automation, and decision-making initiatives.
Unlike traditional database-focused programs, this workshop emphasizes modern cloud-native architectures, scalable data platforms, automation-driven data workflows, and business-focused implementation strategies. The course combines technical concepts with practical business applications, making it valuable for both technical and non-technical professionals involved in data initiatives.
Through real-world case studies, architecture discussions, data flow simulations, process mapping exercises, and implementation planning workshops, participants will develop the
confldence to contribute to data transformation initiatives and support modern data-driven organizations.
By the end of the program, participants will create a Modern Data Architecture Blueprint and a 90-Day Data Engineering Adoption Roadmap.
Why Join This Course?
• Understand modern data engineering fundamentals
• Learn how data pipelines support business intelligence and analytics
• Explore cloud-based data platforms and architectures
• Understand data lakes, warehouses, and modern storage systems
• Improve data quality, reliability, and governance practices
• Learn how organizations build scalable data ecosystems
• Support AI, analytics, and automation initiatives with quality data
• Understand modern ETL and ELT approaches
• Develop practical data engineering implementation plans
• Recognition – Certiflcate of Completion + 20 Executive Education Credits
Learning Objectives
Participants will be able to:
• Understand modern data engineering concepts and architectures
• Identify key components of data ecosystems
• Understand ETL, ELT, and data pipeline frameworks
• Evaluate cloud-based data platforms and storage solutions
• Improve data quality, governance, and reliability practices
• Understand batch and real-time data processing approaches
• Design scalable data workflows and architectures
• Support analytics, AI, and Machine Learning initiatives through data engineering
• Build practical data engineering roadmaps
• Create a structured data architecture blueprint
Target Audience
• Data Analysts
• Data Engineers
• Business Intelligence Professionals
• Analytics Managers
• IT Managers
• Digital Transformation Leaders
• Technology Consultants
• Database Professionals
• Operations & Reporting Teams
• Professionals involved in data initiatives
Certiflcation & Credits
Participants will receive the Certiflcate of Completion from Skelora Edu Tech along with 20 Executive Education Credits (EEC).
Trainer Proflle
Delivered by experienced Data Engineering, Cloud Architecture, Analytics, and Digital Transformation professionals with extensive experience designing and implementing modern data ecosystems. Trainers combine technical expertise with practical business knowledge to ensure immediate workplace application.
The program emphasizes real-world implementation, modern architecture practices, cloud-native thinking, governance frameworks, and scalable data solutions that support organizational growth and innovation.
Note
Pre-training and post-training activities each carry 2 Executive Education Credits and are designed to strengthen implementation readiness, learning retention, and workplace application.
Participants will complete a Modern Data Architecture Blueprint and a 90-Day Data Engineering Adoption Roadmap as part of the program deliverables.
Agenda
Pre-Training Workbook (2 Credits)
Info: Participants will complete a structured readiness assessment before attending the workshop.
Activities include:
• Data Environment Assessment
• Current Architecture Review
• Data Quality Reflection
• Reporting & Analytics Readiness Evaluation
• Data Integration Challenges Assessment
• Cloud Readiness Reflection
• Business Data Needs Analysis
• Learning Goal Development
This preparation ensures participants arrive with a clear understanding of their organization's current data landscape and future opportunities.
Training Day Structure – 16 Modules (16 Hours, 16 Credits)
Day 1 – Foundations of Modern Data Engineering
Module 1: Introduction to Modern Data Engineering
Info: • Evolution of data management
• The role of data engineering in modern organizations
• Data-driven business transformation
• Current industry trends and future outlook
Module 2: Data Architecture Fundamentals
Info: • Understanding modern data architectures
• Data ecosystem components
• Data flow design principles
• Enterprise data architecture concepts
Module 3: Data Sources & Data Integration
Info: • Structured and unstructured data
• Internal and external data sources
• Data integration fundamentals
• Data movement strategies
Module 4: ETL & ELT Essentials
Info: • Understanding ETL workflows
• Modern ELT architectures
• Data transformation concepts
• Business use cases and implementation scenarios
Module 5: Data Warehouses & Data Lakes
Info: • Data storage evolution
• Modern warehouse architectures
• Data lake fundamentals
• Choosing the right storage strategy
Module 6: Data Quality & Reliability
Info: • Data quality frameworks
• Data validation techniques
• Managing data consistency
• Improving trust in business data
Module 7: Cloud Data Platforms
Info: • Cloud data ecosystem overview
• Modern cloud architecture principles
• Scalability and performance considerations
• Cloud adoption strategies
Module 8: Data Engineering Workshop
Info: • Architecture review exercise
• Data flow mapping
• Data quality assessment
• Data modernization planning
Day 2 – Advanced Data Engineering & Implementation
Module 9: Data Pipelines & Workflow Orchestration
Info: • Pipeline design principles
• Workflow automation concepts
• Pipeline monitoring and maintenance
• Operational best practices
Module 10: Real-Time & Streaming Data Concepts
Info: • Batch vs real-time processing
• Event-driven architectures
• Streaming fundamentals
• Business applications of real-time data
Module 11: Data Governance & Compliance
Info: • Governance frameworks
• Data stewardship principles
• Privacy and compliance considerations
• Building trusted data environments
Module 12: Data Security & Risk Management
Info: • Data protection strategies
• Access control principles
• Security best practices
• Risk mitigation frameworks
Module 13: Supporting Analytics, AI & Machine Learning
Info: • Data engineering for analytics
• Preparing data for Machine Learning
• Supporting AI initiatives
• Data readiness frameworks
Module 14: Data Engineering Metrics & Performance
Info: • Measuring data platform effectiveness
• Data reliability indicators
• Operational KPIs
• Continuous improvement strategies
Module 15: Enterprise Data Engineering Roadmaps
Info: • Prioritization frameworks
• Scaling data initiatives
• Building organizational capability
• Long-term data strategy development
Module 16: Modern Data Engineering Capstone
Info: • Data architecture blueprint creation
• Roadmap development
• Peer review and discussion
• 90-Day implementation planning
Post-Training Resource Pack (2 Credits)
Info: Participants will receive a comprehensive implementation toolkit designed to support data engineering initiatives within their organizations.
The resource pack includes:
• 90-Day Data Engineering Challenge
• Data Architecture Assessment Templates
• Data Quality Improvement Toolkit
• Data Pipeline Planning Worksheets
• Governance Readiness Checklist
• Data Integration Planning Framework
• Data Engineering KPI Tracker
• Cloud Readiness Assessment Guide
• Data Modernization Roadmap Planner
• Continuous Improvement Workbook
Where is it happening?
Event Location & Nearby Stays:
USD 1369.67 to USD 1597.81
