course | Advanced Data Engineering – Cloud & On-Prem
Join the Advanced Data E... course taught in English
DIGTR-3173 | Advanced Data Engineering – Cloud & On-Prem
Course Sector : Digital Transformation and Innovation
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 18/10/2026 | 22/10/2026 | Online | $2,150 | Book now |
| 5 Days | 29/03/2027 | 02/04/2027 | Dubai | $4,250 | Book now |
| 5 Days | 09/05/2027 | 13/05/2027 | Riyadh | $4,250 | Book now |
| 5 Days | 16/08/2027 | 20/08/2027 | Istanbul | $4,950 | Book now |
Course Introduction
Data engineering at scale requires robust solutions that handle massive, diverse datasets efficiently while ensuring high availability, performance, and security.
This advanced course equips participants with practical skills for designing, building, and optimizing enterprise-grade data pipelines and architectures across both cloud and on-premise environments. Participants will gain hands-on knowledge in modern tools, distributed processing frameworks, and best practices for data integration and orchestration.
The course emphasizes real-world applications, including batch and real-time processing, cloud-native solutions, and performance optimization.
By the end of the program, participants will be able to design scalable, secure, and high-performing data systems that support analytics, machine learning, and enterprise decision-making.
Course objective
- Design and implement scalable, high-performance data pipelines in cloud and on-prem environments.
- Optimize ETL/ELT processes and distributed data processing frameworks.
- Integrate structured, semi-structured, and unstructured data for analytics and AI.
- Ensure data quality, monitoring, and governance in enterprise pipelines.
- Apply best practices for cloud-native and hybrid data architectures.
Course audience
- Data Engineers and Senior Data Engineers
- Cloud Data Engineers
- Data Architects and Solution Architects
Course Outline | DAY 01
Modern Data Architecture & Pipeline Design
- Designing scalable enterprise data pipelines
- Batch vs. real-time processing frameworks
- Hybrid cloud/on-prem architectures
- ETL/ELT strategies and workflow orchestration
- Best practices in data ingestion from multiple sources
Course Outline | Day 02
Cloud Data Engineering
- Cloud storage and compute options (AWS, Azure, GCP)
- Serverless vs. containerized data pipelines
- Cloud data lakes, warehouses, and lakehouse architecture
- Cost optimization and resource management
- Security, access control, and compliance in cloud environments
Course Outline | Day 03
Big Data Processing & Analytics
- Distributed computing with Apache Spark, Flink, and Kafka
- Real-time streaming pipelines and event-driven architecture
- Handling large-scale unstructured and semi-structured data
- Performance tuning and parallel processing techniques
- Integration with analytics and ML workflows
Course Outline | Day 04
Data Quality, Governance & Monitoring
- Data validation, cleansing, and transformation techniques
- Implementing monitoring and alerting for pipeline health
- Metadata management and lineage tracking
- Logging, observability, and performance metrics
- Ensuring reproducibility and data reliability
Course Outline | Day 05
Advanced Use Cases & Optimization
- End-to-end solution design for complex enterprise use cases
- Pipeline performance benchmarking and optimization
- Event-driven architectures and orchestration for ML data workflows
- Disaster recovery, backup, and high availability strategies
- Hands-on exercises with enterprise datasets
BOOST’s Professional Attendance Certificate “BPAC”
BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.
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