Real-time Data Streaming Architectures Training Course: High-volume Data Systems

Introduction

Master the intricacies of high-velocity data with our Real-Time Data Streaming Architectures Training Course. This program is meticulously designed to equip you with the essential skills to design systems for high-volume real-time data processing, enabling you to build robust and responsive data streaming applications. In today's fast-paced digital world, the ability to handle and analyze real-time data is crucial for delivering immediate insights and driving timely actions. Our real-time data streaming training course provides hands-on experience and expert guidance, empowering you to build scalable and efficient streaming architectures.

This data streaming architectures training delves into the core concepts of real-time data processing, covering topics such as message brokers, stream processing engines, and fault-tolerant system design. You'll gain expertise in using industry-standard tools and techniques to build systems for high-volume real-time data processing that meet the demands of modern applications. Whether you're a data engineer, architect, or developer, this real-time data streaming course will empower you to build powerful streaming solutions.

Target Audience:

• Data Engineers
• Data Architects
• Software Developers
• Big Data Developers
• System Architects
• Streaming Application Developers
• Anyone needing real-time data streaming skills

Course Objectives:

• Understand the fundamentals of real-time data streaming architectures.
• Master the design and implementation of high-volume data processing systems.
• Utilize message brokers for reliable data ingestion and distribution.
• Implement stream processing engines for real-time data transformation and analysis.
• Design fault-tolerant and scalable streaming architectures.
• Utilize cloud-based streaming services for efficient data processing.
• Troubleshoot and optimize real-time data streaming systems.
• Implement data security and access control in streaming architectures.
• Integrate real-time data streams with various data storage and analytics systems.
• Understand how to monitor and maintain real-time streaming systems.
• Explore advanced streaming patterns and techniques.
• Apply real world use cases for real-time data streaming.
• Design systems that handle back pressure and data loss.

Duration

10 Days

Course content

Module 1: Introduction to Real-Time Data Streaming Architectures

• Fundamentals of real-time data streaming.
• Overview of streaming architectures and patterns.
• Setting up a development environment for streaming applications.
• Introduction to streaming tools and frameworks.
• Best practices for streaming architecture.

Module 2: Message Brokers for Data Ingestion

• Utilizing Apache Kafka for high-throughput messaging.
• Implementing RabbitMQ for reliable message queuing.
• Utilizing cloud-based messaging services (AWS Kinesis, Azure Event Hubs).
• Implementing message serialization and deserialization.
• Best practices for message broker usage.

Module 3: Stream Processing Engines

• Utilizing Apache Flink for stateful stream processing.
• Implementing Apache Spark Streaming for batch and stream processing.
• Utilizing Apache Kafka Streams for real-time data transformations.
• Implementing cloud-based stream processing services (Google Dataflow).
• Best practices for stream processing.

Module 4: Fault-Tolerant Streaming Architectures

• Designing fault-tolerant streaming systems.
• Implementing data replication and recovery.
• Utilizing checkpointing and state management.
• Handling stream processing failures and restarts.
• Best practices for fault tolerance.

Module 5: Scalable Streaming Architectures

• Designing scalable streaming systems for high-volume data.
• Implementing data partitioning and sharding.
• Utilizing horizontal scaling and load balancing.
• Optimizing streaming applications for performance.
• Best practices for scalability.

Module 6: Cloud-Based Streaming Services

• Utilizing cloud-based streaming services (AWS Kinesis, Azure Event Hubs, Google Dataflow).
• Implementing serverless streaming architectures.
• Optimizing cloud resources for streaming applications.
• Best practices for cloud-based streaming.

Module 7: Troubleshooting and Optimization

• Debugging streaming applications and pipelines.
• Analyzing streaming system logs and metrics.
• Utilizing monitoring tools and dashboards.
• Optimizing streaming performance and resource utilization.
• Best practices for troubleshooting.

Module 8: Data Security and Access Control

• Implementing data security in streaming architectures.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Implementing data auditing and compliance.
• Best practices for data security.

Module 9: Integration with Data Systems

• Integrating real-time data streams with databases and data lakes.
• Utilizing data connectors and APIs.
• Implementing real-time analytics dashboards.
• Integrating streaming data with machine learning models.
• Best practices for integration.

Module 10: Monitoring and Maintenance

• Monitoring streaming system performance and health.
• Implementing alerting and notifications.
• Utilizing monitoring tools and techniques.
• Implementing streaming system maintenance strategies.
• Best practices for monitoring.

Module 11: Advanced Streaming Patterns

• Implementing event-driven architectures.
• Utilizing change data capture (CDC).
• Implementing stream processing for complex event processing (CEP).
• Utilizing windowing and aggregation techniques.
• Advanced techniques for real-time streaming.

Module 12: Real-World Use Cases

• Implementing real-time fraud detection systems.
• Utilizing streaming data for IoT applications.
• Implementing real-time analytics for e-commerce.
• Utilizing streaming data for log analytics.
• Best practices for real world applications.

Module 13: Streaming on Cloud Platforms

• Deploying streaming architectures on cloud platforms (AWS, Azure, GCP).
• Utilizing cloud-based streaming services.
• Optimizing cloud resources for streaming.
• Best practices for cloud deployment.

Module 14: Streaming and Data Governance

• Implementing data governance policies in streaming architectures.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Real-Time Data Streaming

• Emerging trends in real-time data streaming.
• Utilizing AI and automation in streaming architectures.
• Implementing edge streaming and processing.
• Best practices for future streaming architectures.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.