Batch Data Processing and Pipeline Engineering Training Course

 Batch Data Processing and Pipeline Engineering Training Course 

Introduction 

Batch Data Processing and Pipeline Engineering Training Course provides comprehensive practical knowledge and technical skills in designing, developing, managing, and optimizing modern data processing pipelines for enterprise environments. The course explores advanced batch processing architectures, data ingestion, Extract, Transform, Load (ETL), Extract, Load, Transform (ELT), workflow orchestration, distributed computing, data integration, and automated pipeline development. Participants gain practical experience using industry-standard technologies such as Apache Spark, Apache Hadoop, Apache Airflow, Python, SQL, and cloud-based data engineering platforms to build scalable, reliable, and high-performance batch data processing systems. Emphasis is placed on data quality management, pipeline automation, fault tolerance, data governance, performance optimization, and efficient processing of large-scale datasets. 

The training also examines modern data engineering practices, including incremental data processing, data partitioning, metadata management, scheduling, monitoring, and enterprise data warehouse integration. Participants explore real-world applications of batch processing across financial services, telecommunications, healthcare, e-commerce, logistics, and government institutions. Through practical exercises and international case studies, learners develop the ability to implement resilient data pipelines, improve operational efficiency, reduce processing costs, and support data-driven decision-making. The course incorporates modern cloud data architectures, big data technologies, DevOps principles, and automated workflow management to strengthen organizational data infrastructure and support digital transformation initiatives. 

Course Objectives 

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

1. Understand advanced batch data processing architectures and engineering principles.
2. Design scalable enterprise data pipelines using modern data engineering technologies.
3. Implement ETL and ELT workflows for efficient data integration.
4. Develop automated batch processing applications using Python and SQL.
5. Apply Apache Spark and Hadoop for distributed data processing.
6. Configure workflow orchestration and scheduling using Apache Airflow.
7. Implement data validation, cleansing, and quality assurance techniques.
8. Apply incremental loading, partitioning, and data transformation strategies.
9. Optimize pipeline performance, scalability, and resource utilization.
10. Implement fault tolerance, error handling, and pipeline recovery mechanisms.
11. Integrate cloud-based storage and data processing platforms.
12. Apply data governance, security, and metadata management practices.
13. Monitor, troubleshoot, and maintain enterprise batch processing pipelines.

Organizational Benefits
 

1. Improved enterprise data processing efficiency and reliability.
2. Reduced operational costs through automated workflows.
3. Enhanced data quality and consistency across departments.
4. Faster delivery of business intelligence and analytical reports.
5. Improved scalability of enterprise data infrastructure.
6. Reduced manual intervention in repetitive data operations.
7. Strengthened data governance and regulatory compliance.
8. Improved integration of distributed enterprise data sources.
9. Enhanced business intelligence and strategic decision-making.
10. Increased organizational readiness for big data and cloud transformation.

Target Audiences
 

1. Data engineers and data pipeline developers.
2. Big data architects and enterprise data architects.
3. Database administrators and SQL developers.
4. Business intelligence and analytics professionals.
5. Cloud engineers and data platform administrators.
6. Software developers and Python programmers.
7. IT managers and data infrastructure specialists.
8. Data scientists and machine learning engineers.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Batch Data Processing
 

• Batch processing concepts, architectures, and enterprise applications.
• Differences between batch, streaming, and real-time data processing.
• Data ingestion, storage, transformation, and delivery workflows.
• Batch scheduling, execution cycles, and processing dependencies.
• Data processing architectures used in modern enterprises.
• Case study: Netflix's large-scale data processing and analytics infrastructure.

Module 2: Data Pipeline Architecture and Engineering
 

• Principles of scalable and modular data pipeline architecture.
• Designing reliable ingestion and transformation workflows.
• Pipeline dependencies, components, and execution frameworks.
• Data integration across relational and non-relational databases.
• Pipeline design patterns, reusability, and maintainability.
• Case study: Uber's enterprise data pipeline architecture.

Module 3: ETL and ELT Pipeline Development
 

• Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) methodologies.
• Data extraction from APIs, databases, and enterprise applications.
• Data cleansing, normalization, and transformation techniques.
• Automated loading into data warehouses and data lakes.
• Incremental extraction and change data capture fundamentals.
• Case study: Walmart's enterprise data integration and supply chain analytics.

Module 4: Distributed Batch Processing Technologies
 

• Introduction to Apache Hadoop and distributed processing.
• Apache Spark architecture and batch processing operations.
• Distributed datasets, parallel execution, and resource management.
• Processing large-scale structured and semi-structured datasets.
• Performance optimization through partitioning and caching.
• Case study: LinkedIn's distributed data processing ecosystem.

Module 5: Workflow Orchestration and Automation
 

• Workflow orchestration concepts and pipeline scheduling.
• Developing automated workflows using Apache Airflow.
• Configuring Directed Acyclic Graphs (DAGs) and task dependencies.
• Scheduling, retries, logging, and execution monitoring.
• Integrating Python scripts and SQL processing tasks.
• Case study: Airbnb's use of Apache Airflow for data workflow orchestration.

Module 6: Data Quality, Validation, and Governance
 

• Data profiling, validation, and quality assessment frameworks.
• Identifying missing values, duplicates, and data inconsistencies.
• Implementing automated data quality checks.
• Data lineage, metadata management, and documentation.
• Data security, access control, and regulatory compliance.
• Case study: European financial institutions and enterprise data governance practices.

Module 7: Pipeline Monitoring and Performance Optimization
 

• Monitoring batch execution and pipeline performance.
• Identifying processing bottlenecks and operational failures.
• Implementing error handling and automated recovery.
• Optimizing memory, storage, and computational resources.
• Logging, alerting, and pipeline performance reporting.
• Case study: Spotify's data infrastructure and large-scale processing optimization.

Module 8: Cloud Data Engineering and Enterprise Deployment
 

• Cloud-based batch processing architecture and infrastructure.
• Integrating Amazon Web Services, Microsoft Azure, and Google Cloud.
• Deploying automated data pipelines and enterprise data workflows.
• Data lake, data warehouse, and cloud storage integration.
• Pipeline testing, version control, and DevOps practices.
• Case study: Capital One's cloud-based data engineering transformation.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard data engineering tools.
• Hands-on batch processing and pipeline development exercises.
• Group assignments involving enterprise data integration scenarios.
• International case study analysis and technical problem-solving.
• Practical assessments, knowledge checks, and final 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.