Data Engineering Performance Optimization Training Course

 Data Engineering Performance Optimization Training Course 

Introduction 

Data Engineering Performance Optimization Training Course is designed to equip professionals with advanced technical knowledge and practical skills for improving the performance, scalability, reliability, and efficiency of modern data engineering systems. As organizations increasingly adopt big data analytics, artificial intelligence, machine learning, cloud computing, and real-time data processing, optimizing data infrastructure has become essential for achieving operational excellence and competitive advantage. This course explores advanced data engineering optimization techniques, including data pipeline performance tuning, distributed computing, database query optimization, data warehouse efficiency, parallel processing, memory management, and cloud-based data infrastructure optimization. Participants will gain practical experience in identifying performance bottlenecks, reducing data processing latency, improving resource utilization, and implementing scalable data architectures. 

The training also examines emerging technologies and industry best practices for building high-performance data ecosystems capable of supporting complex analytical workloads and enterprise data operations. Participants will explore Apache Spark optimization, SQL performance tuning, workflow orchestration, data lakehouse optimization, streaming data architecture, and automated performance monitoring. Through practical exercises and international case studies, including large-scale data infrastructure implementations at Netflix, Amazon, Google, and Uber, participants will develop the expertise required to improve data processing efficiency, minimize infrastructure costs, strengthen data reliability, and support data-driven decision-making. The course emphasizes modern data engineering strategies that enable organizations to achieve sustainable digital transformation and maximize the value of enterprise data assets. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand advanced data engineering performance optimization principles and methodologies.
2. Design scalable and high-performance data engineering architectures.
3. Optimize Apache Spark workloads and distributed data processing.
4. Apply advanced SQL query optimization and database indexing techniques.
5. Improve Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipeline performance.
6. Implement efficient cloud data engineering and resource management strategies.
7. Optimize real-time data streaming and event-driven architectures.
8. Identify and resolve data processing bottlenecks and latency issues.
9. Apply parallel computing and distributed processing techniques.
10. Improve data warehouse and data lakehouse performance.
11. Implement automated data pipeline monitoring and performance testing.
12. Reduce cloud infrastructure expenditure through data workload optimization.
13. Apply artificial intelligence and automation in modern data engineering operations.

Organizational Benefits
 

1. Reduced data processing costs and infrastructure expenditure.
2. Improved enterprise data pipeline efficiency and reliability.
3. Faster business intelligence and analytics delivery.
4. Enhanced scalability of enterprise data platforms.
5. Improved database performance and query execution.
6. Reduced operational downtime and processing bottlenecks.
7. Better utilization of cloud computing resources.
8. Improved real-time analytics and reporting capabilities.
9. Stronger enterprise data governance and operational resilience.
10. Increased organizational competitiveness through data-driven innovation.

Target Audiences
 

1. Data Engineers and Senior Data Engineers.
2. Big Data Architects and Solutions Architects.
3. Database Administrators and Database Engineers.
4. Cloud Computing and Infrastructure Engineers.
5. Business Intelligence and Analytics Professionals.
6. Machine Learning and Artificial Intelligence Engineers.
7. Data Platform and DevOps Engineers.
8. Information Technology Managers and Technical Consultants.

Course Duration: 5 days

Course Modules

Module 1: Advanced Data Engineering Performance Fundamentals
 

• Principles of data engineering performance optimization.
• Data architecture scalability and workload assessment.
• Performance benchmarking and optimization metrics.
• Identifying processing bottlenecks and resource constraints.
• Data engineering performance lifecycle management.
• Case Study: Google's distributed data infrastructure and large-scale processing optimization.

Module 2: Data Pipeline Performance Optimization
 

• Advanced Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) optimization.
• Pipeline parallelization and task execution strategies.
• Data partitioning, batching, and incremental processing.
• Workflow orchestration using Apache Airflow.
• Pipeline monitoring and automated failure recovery.
• Case Study: Netflix's large-scale data pipeline engineering and optimization.

Module 3: Apache Spark and Distributed Computing Optimization
 

• Apache Spark architecture and execution optimization.
• Memory management, caching, and data serialization.
• Partition management and shuffle optimization.
• Distributed computing and parallel processing techniques.
• Performance tuning using Spark UI and execution plans.
• Case Study: Uber's distributed data processing and Apache Spark implementation.

Module 4: Database and SQL Performance Optimization
 

• Advanced SQL query execution and optimization.
• Database indexing, partitioning, and caching strategies.
• Query execution plans and performance diagnostics.
• Database normalization and workload optimization.
• Transaction management and database resource efficiency.
• Case Study: Amazon's database infrastructure and high-performance query processing.

Module 5: Cloud Data Infrastructure and Cost Optimization
 

• Cloud-native data engineering architecture optimization.
• Resource allocation and workload management.
• Cloud storage and data transfer efficiency.
• Serverless computing and infrastructure automation.
• Cloud monitoring and FinOps optimization strategies.
• Case Study: Google's BigQuery and cloud-based analytical workload optimization.

Module 6: Real-Time Data Streaming Optimization
 

• Real-time data processing architecture and optimization.
• Apache Kafka performance tuning and configuration.
• Stream partitioning and event processing efficiency.
• Latency reduction and throughput improvement.
• Monitoring distributed streaming applications.
• Case Study: LinkedIn's Kafka-based distributed event streaming ecosystem.

Module 7: Data Warehouse and Lakehouse Optimization
 

• Modern data warehouse performance strategies.
• Data lakehouse architecture and storage optimization.
• Columnar storage formats and compression techniques.
• Query acceleration and analytical workload management.
• Data lifecycle management and storage efficiency.
• Case Study: Databricks' lakehouse architecture and enterprise analytics optimization.

Module 8: Performance Monitoring, Testing, and Automation
 

• Data engineering performance testing frameworks.
• Automated monitoring and observability tools.
• Data quality validation and reliability engineering.
• Artificial intelligence-driven performance optimization.
• Continuous integration and continuous delivery for data pipelines.
• Case Study: Airbnb's data infrastructure monitoring and large-scale data reliability practices.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical laboratory exercises using industry-standard data engineering tools.
• Hands-on Apache Spark, SQL, and Apache Kafka optimization exercises.
• Group discussions and collaborative problem-solving activities.
• International industry case studies and benchmarking exercises.
• Practical performance assessment, troubleshooting, and optimization projects.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.