Building Streaming Data Pipelines Training Course

 Building Streaming Data Pipelines Training Course 

Introduction 

Building Streaming Data Pipelines Training Course provides comprehensive practical knowledge and technical skills for designing, developing, deploying, and managing real-time data streaming infrastructures. The course explores modern big data engineering, real-time analytics, event-driven architecture, distributed data processing, and scalable data integration technologies. Participants gain practical experience with Apache Kafka, Apache Spark Structured Streaming, Apache Flink, Apache Pulsar, and cloud-based streaming platforms to build reliable, high-performance data pipelines. The programme examines data ingestion, event processing, stream transformation, data synchronization, message brokers, fault tolerance, and real-time monitoring to support modern enterprise data management and digital transformation initiatives. 

The training further explores advanced streaming architectures, data quality management, microservices integration, real-time dashboards, and automated data processing workflows. Participants learn how to implement secure, scalable, and resilient streaming solutions for financial services, telecommunications, healthcare, manufacturing, logistics, and e-commerce environments. Through practical exercises and global industry case studies, learners develop competencies in continuous data integration, low-latency processing, stream analytics, and cloud-native data engineering. The course prepares professionals to optimize business intelligence, predictive analytics, operational intelligence, and data-driven decision-making through modern streaming data pipeline technologies. 

Course Objectives 

• Understand streaming data architecture and real-time processing fundamentals.
• Develop scalable data ingestion and integration pipelines.
• Implement Apache Kafka and distributed messaging systems.
• Apply Apache Spark Structured Streaming for real-time analytics.
• Configure Apache Flink for continuous data processing.
• Design event-driven and microservices-based architectures.
• Implement real-time data transformation and enrichment.
• Apply data quality, validation, and governance techniques.
• Configure fault tolerance and pipeline recovery mechanisms.
• Integrate streaming pipelines with cloud computing platforms.
• Implement real-time monitoring and performance optimization.
• Strengthen streaming data security and access management.
• Develop production-ready streaming data engineering solutions.

Organizational Benefits
 

• Improved real-time business intelligence and operational visibility.
• Faster data-driven decision-making and business responsiveness.
• Enhanced enterprise data integration and interoperability.
• Reduced data processing delays and operational inefficiencies.
• Improved customer experience through real-time personalization.
• Strengthened data quality and governance frameworks.
• Increased scalability of enterprise data infrastructure.
• Improved fraud detection and risk monitoring capabilities.
• Enhanced predictive analytics and business forecasting.
• Reduced infrastructure costs through optimized data processing.

Target Audiences
 

1. Data engineers and big data professionals.
2. Data scientists and machine learning specialists.
3. Business intelligence and analytics professionals.
4. Software developers and application architects.
5. Database administrators and data integration specialists.
6. Cloud computing and DevOps engineers.
7. Information technology managers and enterprise architects.
8. Digital transformation and technology consultants.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Streaming Data Pipelines
 

• Streaming data concepts and real-time processing architectures.
• Batch processing versus stream processing.
• Event-driven architecture and distributed systems.
• Streaming data lifecycle and pipeline components.
• Data ingestion patterns and message delivery mechanisms.
• Case study: Netflix real-time data processing architecture.

Module 2: Streaming Data Ingestion and Message Brokers
 

• Introduction to Apache Kafka and Apache Pulsar.
• Topics, partitions, brokers, producers, and consumers.
• Data serialization using JSON, Avro, and Protobuf.
• Implementing message queues and event streaming.
• Configuring reliable data ingestion workflows.
• Case study: LinkedIn's Kafka-based enterprise data streaming ecosystem.

Module 3: Apache Spark Structured Streaming
 

• Introduction to Apache Spark Structured Streaming.
• Developing real-time data processing applications.
• DataFrame and Dataset operations for streaming.
• Window operations, triggers, and event-time processing.
• Checkpointing and stateful stream processing.
• Case study: Uber's large-scale real-time data processing applications.

Module 4: Apache Flink and Advanced Stream Processing
 

• Apache Flink architecture and distributed processing.
• Stateful computations and event-time semantics.
• Watermarks, windows, and late-arriving data.
• Stream joins and continuous data transformations.
• Fault tolerance and exactly-once processing.
• Case study: Alibaba's real-time computing and streaming analytics infrastructure.

Module 5: Data Transformation, Integration and Enrichment
 

• Real-time data cleansing and transformation techniques.
• Data enrichment using external data sources.
• Integrating APIs, databases, and streaming platforms.
• Implementing Change Data Capture (CDC).
• Data validation and schema evolution management.
• Case study: Financial transaction streaming and enrichment at global banking institutions.

Module 6: Cloud-Based Streaming Data Pipelines
 

• Introduction to Amazon Kinesis and AWS streaming services.
• Implementing Azure Event Hubs and cloud data integration.
• Google Cloud Pub/Sub and Dataflow architectures.
• Containerized streaming applications using Docker and Kubernetes.
• Cloud scalability, storage integration, and deployment strategies.
• Case study: Spotify's cloud-enabled data processing and recommendation ecosystem.

Module 7: Pipeline Security, Monitoring and Optimization
 

• Streaming pipeline security and access control.
• Monitoring latency, throughput, and processing performance.
• Logging, alerting, and observability frameworks.
• Managing failures, recovery, and data consistency.
• Resource optimization and high-availability configurations.
• Case study: Real-time fraud detection and transaction monitoring in digital banking.

Module 8: Streaming Pipeline Deployment and Capstone Project
 

• Designing production-ready streaming data architectures.
• Implementing end-to-end streaming pipeline workflows.
• Integrating streaming data with analytical dashboards.
• Testing, debugging, and validating pipeline performance.
• Deployment strategies and continuous integration practices.
• Case study: Building a real-time logistics tracking pipeline inspired by global supply chain platforms.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard streaming technologies.
• Hands-on Apache Kafka, Spark, and Flink laboratory exercises.
• Real-world enterprise case studies and group assignments.
• Individual streaming pipeline development projects.
• Collaborative problem-solving and technical troubleshooting.
• Practical assessments, demonstrations, and knowledge-sharing sessions.

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.