Advanced ETL and ELT Development Training Course

 Advanced ETL and ELT Development Training Course 

Introduction 

Advanced ETL and ELT Development Training Course provides comprehensive practical knowledge and technical expertise in modern data integration, extraction, transformation, and loading processes. The course explores advanced Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) architectures, cloud-native data engineering, enterprise data pipelines, data orchestration, real-time data processing, and distributed computing. Participants gain practical skills in designing scalable data integration frameworks, developing automated workflows, implementing data quality controls, and optimizing high-performance data pipelines using modern technologies such as Apache Airflow, dbt, Apache Spark, Python, SQL, and cloud data platforms. 

The training emphasizes enterprise data architecture, big data integration, cloud data warehousing, data lakehouse engineering, and modern analytics infrastructure. Through practical exercises and international case studies, participants explore advanced pipeline development, change data capture, incremental loading, metadata management, workflow automation, and data governance. The course also examines real-world applications of ETL and ELT technologies across financial services, healthcare, telecommunications, e-commerce, and government institutions, equipping professionals with the capabilities to implement reliable, secure, and cost-effective data engineering solutions that support business intelligence, artificial intelligence, machine learning, and data-driven decision-making. 

Course Objectives 

1. Master advanced ETL and ELT architectures and development methodologies.
2. Design scalable enterprise data integration pipelines.
3. Implement cloud-native data engineering solutions.
4. Develop automated workflows using Apache Airflow.
5. Optimize SQL-based data transformation and processing.
6. Apply Python programming for advanced data integration.
7. Implement real-time data ingestion and streaming pipelines.
8. Integrate structured, semi-structured, and unstructured datasets.
9. Apply change data capture and incremental loading techniques.
10. Implement enterprise data quality and validation frameworks.
11. Strengthen data security, governance, and regulatory compliance.
12. Optimize distributed processing using Apache Spark.
13. Develop modern data lakehouse and cloud warehousing solutions.

Organizational Benefits
 

1. Improved enterprise data integration and operational efficiency.
2. Reduced data processing costs and infrastructure expenditure.
3. Enhanced business intelligence and reporting capabilities.
4. Improved data accuracy, consistency, and reliability.
5. Faster access to real-time business information.
6. Strengthened enterprise data governance and security.
7. Increased scalability of organizational data infrastructure.
8. Improved artificial intelligence and machine learning readiness.
9. Reduced manual data processing and operational errors.
10. Enhanced strategic planning through data-driven decision-making.

Target Audience
 

1. Data engineers and ETL developers.
2. Database administrators and architects.
3. Business intelligence professionals.
4. Data architects and enterprise solution designers.
5. Cloud computing and DevOps engineers.
6. Data scientists and analytics specialists.
7. Software developers and integration specialists.
8. IT managers and technology consultants.

Course Duration: 10 days

Course Modules

Module 1: Advanced ETL and ELT Architecture
 

• Enterprise ETL and ELT architecture principles.
• Data integration patterns and architectural frameworks.
• Batch processing versus streaming architectures.
• Scalable pipeline design and implementation.
• Data warehouse and data lake integration.
• Case study: Netflix's large-scale data engineering architecture.

Module 2: Advanced Data Extraction Techniques
 

• Database extraction using advanced SQL.
• API-based and web-based data ingestion.
• Extracting structured and unstructured datasets.
• Incremental extraction and change data capture.
• Automated data acquisition frameworks.
• Case study: Amazon's large-scale e-commerce data integration.

Module 3: Advanced Data Transformation with Python
 

• Advanced Python programming for data engineering.
• Data manipulation using Pandas and Polars.
• Data cleansing and transformation automation.
• Complex joins, aggregations, and normalization.
• Reusable transformation functions.
• Case study: Spotify's data processing and analytics pipelines.

Module 4: Advanced SQL for ETL and ELT
 

• Complex SQL queries and analytical functions.
• Stored procedures and query optimization.
• Common table expressions and window functions.
• Incremental transformations and data partitioning.
• Performance tuning and indexing strategies.
• Case study: Financial transaction processing in global banking.

Module 5: Cloud-Based ETL and ELT Development
 

• Cloud-native data integration architecture.
• AWS Glue and Azure Data Factory.
• Google Cloud Dataflow integration.
• Cloud storage and warehouse connectivity.
• Scalable cloud pipeline deployment.
• Case study: Cloud data migration in multinational enterprises.

Module 6: Data Pipeline Orchestration and Automation
 

• Apache Airflow architecture and configuration.
• Workflow scheduling and dependency management.
• Pipeline monitoring and automated execution.
• Error handling and retry mechanisms.
• CI/CD for data engineering workflows.
• Case study: Airbnb's data workflow orchestration practices.

Module 7: Advanced Data Warehousing and ELT
 

• Modern cloud data warehouse architecture.
• Snowflake, BigQuery, and Amazon Redshift.
• Dimensional modelling and schema design.
• ELT transformation using dbt.
• Data marts and enterprise reporting.
• Case study: Enterprise analytics using Google BigQuery.

Module 8: Real-Time Data Integration and Streaming
 

• Real-time ETL and streaming architecture.
• Apache Kafka and event-driven pipelines.
• Stream processing using Apache Flink.
• Data synchronization and event processing.
• Streaming data validation and monitoring.
• Case study: Real-time financial fraud detection systems.

Module 9: Big Data Processing with Apache Spark
 

• Distributed data processing architecture.
• Apache Spark DataFrames and Spark SQL.
• Large-scale transformation and aggregation.
• Performance optimization and partitioning.
• Integration with data lakes and warehouses.
• Case study: Large-scale analytics at Netflix.

Module 10: Advanced Data Quality and Validation
 

• Automated data profiling and assessment.
• Data cleansing and deduplication.
• Schema validation and integrity controls.
• Anomaly detection and reconciliation.
• Data quality monitoring frameworks.
• Case study: Healthcare data quality management.

Module 11: Change Data Capture and Incremental Loading
 

• Change data capture architecture.
• Database log-based replication techniques.
• Incremental extraction and loading strategies.
• Data synchronization and replication.
• Handling historical and transactional changes.
• Case study: Banking database synchronization systems.

Module 12: Data Lakehouse and Modern Data Platforms
 

• Data lakehouse architecture and implementation.
• Apache Iceberg and Delta Lake.
• Data cataloguing and metadata management.
• ACID transactions and schema evolution.
• Integration of analytical data platforms.
• Case study: Enterprise lakehouse implementation in retail.

Module 13: Data Security, Governance and Compliance
 

• Enterprise data governance frameworks.
• Data encryption and access management.
• Personally identifiable information protection.
• Audit trails and regulatory compliance.
• Metadata lineage and access monitoring.
• Case study: Financial data governance under international regulatory frameworks.

Module 14: ETL and ELT Performance Optimization
 

• Pipeline bottleneck identification.
• Query execution and resource optimization.
• Parallel processing and workload management.
• Storage optimization and data partitioning.
• Pipeline performance monitoring.
• Case study: Large-scale e-commerce data warehouse optimization.

Module 15: Enterprise ETL and ELT Project Implementation
 

• Designing an end-to-end enterprise pipeline.
• Integrating databases, APIs, and cloud platforms.
• Implementing automated transformation workflows.
• Testing, deployment, and production monitoring.
• Documentation and continuous improvement.
• Case study: Developing an integrated multinational enterprise analytics platform.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical ETL and ELT development exercises.
• Hands-on Python and SQL programming sessions.
• Cloud-based data engineering laboratory exercises.
• Group projects and collaborative pipeline development.
• International case studies and industry simulations.
• Interactive discussions and technical problem-solving.
• Practical assessments and end-of-course project presentations.

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.