course | Data Engineering with Spark Optimization
Join the Data Engineerin... course taught in English
DIGTR-3178 | Data Engineering with Spark Optimization
Course Sector : Digital Transformation and Innovation
| Duration | Date from | Date to | Course Venue | Course fees | Book a course |
|---|---|---|---|---|---|
| 5 Days | 25/10/2026 | 29/10/2026 | Online | $2,150 | Book now |
| 5 Days | 04/04/2027 | 08/04/2027 | Cairo | $4,250 | Book now |
| 5 Days | 23/05/2027 | 27/05/2027 | Riyadh | $4,250 | Book now |
| 5 Days | 16/08/2027 | 20/08/2027 | Dubai | $4,250 | Book now |
Course Introduction
Efficient processing of large-scale datasets requires mastery of distributed computing frameworks like Apache Spark. This program helps participants develop intermediate-level expertise in PySpark, Spark optimization, and performance tuning for ETL workloads, enabling them to manage high-volume, high-velocity data effectively.
Participants will learn to optimize Spark jobs, manage resource allocation, and design robust, scalable pipelines for real-world big data environments.
Course objective
- Develop and optimize Spark jobs for large datasets.
- Implement advanced PySpark transformations and actions.
- Apply performance tuning techniques in distributed data environments.
- Integrate Spark workflows with ETL/ELT pipelines.
- Monitor and troubleshoot Spark workloads effectively.
Course audience
- Data Engineers
- Big Data Engineers
- Data Architects
- ETL Developers
Course Outline | DAY 01
Spark Fundamentals for ETL Workloads
- Spark ecosystem overview: Spark Core, SQL, Streaming
- RDDs, DataFrames, and Datasets
- Transformations vs actions
- Handling structured and unstructured data
- Spark deployment modes
Course Outline | DAY 02
PySpark for Intermediate Data Engineering
- Advanced DataFrame operations and UDFs
- Spark SQL for complex queries
- Handling large datasets efficiently
- Error handling and logging in PySpark
- Integrating PySpark with Python ETL scripts
Course Outline | DAY 03
Spark Optimization Techniques
- Partitioning and caching strategies
- Join optimization and shuffle management
- Broadcast joins and map-side reductions
- Memory management and tuning
- Performance benchmarking and profiling
Course Outline | DAY 04
ETL Pipeline Integration
- Designing ETL pipelines using PySpark
- Incremental and batch processing strategies
- Pipeline orchestration with Airflow/Spark-submit
- Logging, monitoring, and alerting
- Ensuring data quality and integrity
Course Outline | DAY 05
Advanced Data Engineering Patterns
- Optimizing workflows for cloud and on-prem clusters
- Handling streaming data with Spark Structured Streaming
- Reusable pipeline components
- Case studies: big data pipeline architectures
- Best practices for production-ready Spark workflows
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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