Data Engineering Essentials: Building Scalable Data Pipelines
A hands-on 5-day intensive course designed for professionals transitioning into data engineering or expanding their technical skills. Learn to design, build, and manage scalable data pipelines using industry-standard tools and frameworks, with direct application to real-world enterprise challenges. Gain practical expertise in data architecture, ETL processes, and modern data stack technologies.
About the Course
Data engineering has become critical to organizational success, driving real-time analytics, business intelligence, and machine learning initiatives. This intensive 5-day course bridges the gap between foundational data knowledge and production-ready engineering skills. You'll work with modern data stack technologies and frameworks used by leading enterprises, gaining hands-on experience through practical projects and real-world scenarios.
Course Objectives
By the end of this course, participants will be able to:
- Design and architect scalable data pipelines that handle enterprise-scale workloads
- Implement ETL/ELT processes using industry-standard tools and best practices
- Manage data quality, validation, and monitoring in production environments
- Optimize data storage, query performance, and infrastructure costs
- Build and maintain data platforms that support analytics and machine learning
- Apply cloud-native data engineering patterns and technologies effectively
Target Audience
This course is designed for professionals with some technical foundation looking to specialize in data engineering:
- Developers transitioning into data engineering roles
- Data analysts seeking to expand into pipeline development and infrastructure
- Systems engineers and architects building data platforms
- Technical leads managing data engineering teams
- Professionals with 1-3 years of experience seeking advanced skills
What You Will Benefit as a Learner
Participants gain tangible, immediately applicable skills that impact your organization:
- Career advancement: Master skills in high-demand roles with competitive compensation
- Hands-on expertise: Build and deploy real pipelines, not just theory
- Problem-solving frameworks: Learn patterns for common data engineering challenges
- Cloud proficiency: Work with AWS, Google Cloud, or Azure data services
- Production readiness: Understand monitoring, testing, and operational excellence
- Collaboration skills: Communicate effectively with data scientists, analysts, and stakeholders
Training Methodology
This course combines multiple instructional approaches optimized for adult learning and rapid skill development:
- Project-based learning: Each module includes hands-on labs where you build real solutions to business problems
- Code-along sessions: Instructors demonstrate techniques you immediately replicate and modify
- Real-world case studies: Analyze data engineering solutions from actual enterprise implementations
- Peer collaboration: Work in small groups on challenges, sharing approaches and solutions
- Rapid feedback: Get immediate guidance on your work from experienced instructors
- Resource toolkit: Leave with templates, code snippets, and configuration files for your organization
Frequently Asked Questions
You should have foundational programming knowledge (Python, Java, or similar) and basic SQL experience. Familiarity with cloud platforms is helpful but not required—we cover cloud essentials in Module 2. If you have data analysis, software development, or IT operations experience, you'll adapt quickly. The course is designed for people with approximately 1-3 years of technical experience looking to specialize in data engineering.
The course covers cloud-agnostic principles while providing hands-on labs with one primary platform (typically AWS, though GCP and Azure variations are available). Core concepts around data warehousing, storage, and services transfer across cloud providers. We focus on building transferable skills, so you'll understand how to apply patterns regardless of your organization's cloud choice.
Yes, absolutely. Labs use realistic datasets that simulate enterprise scenarios—sales transactions, user events, IoT sensor data, and more. You'll build end-to-end pipelines against real data while handling common challenges like duplicates, late arrivals, and schema variations. We anonymize production data to create authentic learning experiences without compliance concerns.
The course covers the full spectrum of skills enterprise data engineering roles require: architecture, implementation, testing, monitoring, and operations. You'll build portfolio projects you can showcase to employers, understand technical interview topics, and learn patterns used by Fortune 500 companies. Most participants report confidence tackling production data engineering tasks immediately after the course.
Core technologies include Apache Airflow for orchestration, SQL and dbt for transformations, Spark for large-scale processing, Kafka for streaming, and cloud platforms (Snowflake, BigQuery, Redshift). We also cover modern practices like infrastructure-as-code, containerization with Docker, version control with Git, and infrastructure tools. The emphasis is on understanding patterns so you can apply them with any technology stack.
Yes, each technology is covered through practical labs where you implement actual use cases. You'll build Airflow DAGs, write Spark transformations, configure Kafka topics, and integrate them into complete pipelines. Labs progress from guided implementations to open-ended challenges where you make architecture decisions and defend your choices.
Bring your own laptop (Mac, Linux, or Windows with WSL2). We provide cloud sandbox environments so you don't need powerful hardware locally. All tools are open-source or use free cloud tiers, so there's no licensing cost. Setup instructions are provided in advance, and technical support is available on Day 1 to ensure everyone's ready to begin.
You'll receive a completion certificate, all course materials and code samples, reusable pipeline templates, configuration files for cloud setup, and access to an instructor Q&A forum for 30 days post-course. Many organizations also provide job placement assistance and alumni networking opportunities. You'll leave with a foundation for continued learning in specialized data engineering areas.
Duration
Mon-Fri (5 Days)
Level
advanced
Delivery
Flexible Options
Virtual, In-Person, or Self-Paced
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Course Modules
Establish core concepts of data engineering by exploring modern data architecture patterns, the evolution of data platforms, and the modern data stack ecosystem. Understand how data flows through organizations, the role of data engineering in supporting analytics and AI initiatives, and the key decisions architects make when designing platforms. You'll evaluate different architecture patterns and learn when to apply batch processing, stream processing, and hybrid approaches.
Configure professional development and production environments for data engineering work. Set up containerization with Docker, version control workflows, cloud platform essentials (AWS/GCP/Azure), and local development tools. You'll create reproducible environments, understand infrastructure-as-code principles, and establish best practices for managing configurations across development and production stages.
Master SQL for data engineering, going beyond basic queries to focus on transformation logic, window functions, and complex joins used in production pipelines. Learn how to write efficient, maintainable SQL; optimize queries for performance; and structure transformations for clarity and reusability. Apply these skills to real datasets through hands-on labs that mirror enterprise transformation requirements.
Design and build end-to-end ETL and ELT pipelines using modern tools and frameworks. Understand the differences between ETL and ELT approaches, when to choose each, and how to implement them using Apache Airflow, dbt, or cloud-native orchestration services. Build pipelines that extract from diverse sources, transform data according to business logic, and load into target systems with proper error handling.
Explore stream processing architectures and technologies for real-time data pipelines. Learn event-driven design patterns, understand Kafka and other message brokers, and implement streaming jobs using Apache Spark Streaming or Flink. Build pipelines that process continuous data streams, handle late-arriving data, and deliver real-time insights to business stakeholders.
Implement comprehensive data quality frameworks that catch issues before they impact downstream analytics or decisions. Learn data profiling techniques, implement validation rules, design automated tests for data pipelines, and set up monitoring for data quality metrics. Build reliability into pipelines through assertive data contracts and quality gates that ensure data meets business requirements.
Work with cloud-native data platforms including Snowflake, BigQuery, Redshift, or cloud-specific services from AWS, GCP, and Azure. Understand cloud storage architectures (data lakes, lakehouses), managed services for data integration, and cost optimization strategies. Configure and manage cloud data warehouses, implement proper access controls, and leverage cloud services to build scalable infrastructure.
Optimize data pipelines for speed and cost efficiency in production environments. Learn query optimization techniques, indexing strategies, partitioning approaches, and caching patterns that reduce execution time and infrastructure costs. Analyze pipeline performance, identify bottlenecks, and apply optimization strategies that deliver business value without unnecessary spending.
Design monitoring and observability strategies for production data pipelines that enable rapid issue detection and resolution. Implement logging, metrics, and alerting frameworks; set up dashboards for pipeline health; and establish incident response procedures. Create operational playbooks that document known issues, recovery procedures, and best practices for supporting data platforms in production.
Address critical enterprise concerns around data security, compliance, and governance. Implement access controls, encryption strategies, and audit logging; understand regulatory requirements (GDPR, CCPA); and design data governance frameworks that balance accessibility with protection. Build systems that support your organization's compliance posture while enabling effective data usage.
Explore advanced data engineering patterns for handling complex scenarios and massive scale. Learn dimensional modeling for analytics, slowly changing dimensions, master-data management approaches, and multi-hop medallion architectures. Understand how to structure pipelines for evolution, maintainability, and extreme scale while keeping code clean and business logic transparent.
Apply everything learned by designing and building a complete data platform solution addressing a realistic business scenario. Work in teams to architect a solution, implement pipeline components, establish governance and quality frameworks, and present your platform to stakeholders. Gain experience in cross-functional collaboration, technical decision-making under constraints, and delivering end-to-end solutions.
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