15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
Session 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.
About the Course
This advanced training provides participants with the knowledge and practical skills required to design, implement, scale, secure, and optimize modern real-time data streaming architectures. The course focuses on how organizations can capture and process continuously generated data to support real-time analytics, operational intelligence, automation, and faster decision-making.Participants will explore event-driven architecture, streaming ingestion, message brokers, stream processing, data integration, state management, scalability, fault tolerance, observability, security, and cloud-native streaming platforms. Emphasis is placed on designing resilient, low-latency architectures capable of handling high-volume and high-velocity data across enterprise environments.
Curriculum & Topics
15 Topics | 71 Sessions
-
Workshop 1.1:
Fundamentals of real-time data streaming.
-
Workshop 1.2:
Overview of streaming architectures and patterns.
-
Workshop 1.3:
Setting up a development environment for streaming applications.
-
Workshop 1.4:
Introduction to streaming tools and frameworks.
-
Workshop 1.5:
Best practices for streaming architecture.
-
Workshop 2.1:
Utilizing Apache Kafka for high-throughput messaging.
-
Workshop 2.2:
Implementing RabbitMQ for reliable message queuing.
-
Workshop 2.3:
Utilizing cloud-based messaging services (AWS Kinesis, Azure Event Hubs).
-
Workshop 2.4:
Implementing message serialization and deserialization.
-
Workshop 2.5:
Best practices for message broker usage.
-
Workshop 3.1:
Utilizing Apache Flink for stateful stream processing.
-
Workshop 3.2:
Implementing Apache Spark Streaming for batch and stream processing.
-
Workshop 3.3:
Utilizing Apache Kafka Streams for real-time data transformations.
-
Workshop 3.4:
Implementing cloud-based stream processing services (Google Dataflow).
-
Workshop 3.5:
Best practices for stream processing.
-
Workshop 4.1:
Designing fault-tolerant streaming systems.
-
Workshop 4.2:
Implementing data replication and recovery.
-
Workshop 4.3:
Utilizing checkpointing and state management.
-
Workshop 4.4:
Handling stream processing failures and restarts.
-
Workshop 4.5:
Best practices for fault tolerance.
-
Workshop 5.1:
Designing scalable streaming systems for high-volume data.
-
Workshop 5.2:
Implementing data partitioning and sharding.
-
Workshop 5.3:
Utilizing horizontal scaling and load balancing.
-
Workshop 5.4:
Optimizing streaming applications for performance.
-
Workshop 5.5:
Best practices for scalability.
-
Workshop 6.1:
Utilizing cloud-based streaming services (AWS Kinesis, Azure Event Hubs, Google Dataflow).
-
Workshop 6.2:
Implementing serverless streaming architectures.
-
Workshop 6.3:
Optimizing cloud resources for streaming applications.
-
Workshop 6.4:
Best practices for cloud-based streaming.
-
Workshop 7.1:
Debugging streaming applications and pipelines.
-
Workshop 7.2:
Analyzing streaming system logs and metrics.
-
Workshop 7.3:
Utilizing monitoring tools and dashboards.
-
Workshop 7.4:
Optimizing streaming performance and resource utilization.
-
Workshop 7.5:
Best practices for troubleshooting.
-
Workshop 8.1:
Implementing data security in streaming architectures.
-
Workshop 8.2:
Utilizing authentication and authorization.
-
Workshop 8.3:
Implementing data encryption and masking.
-
Workshop 8.4:
Implementing data auditing and compliance.
-
Workshop 8.5:
Best practices for data security.
-
Workshop 9.1:
Integrating real-time data streams with databases and data lakes.
-
Workshop 9.2:
Utilizing data connectors and APIs.
-
Workshop 9.3:
Implementing real-time analytics dashboards.
-
Workshop 9.4:
Integrating streaming data with machine learning models.
-
Workshop 9.5:
Best practices for integration.
-
Workshop 10.1:
Monitoring streaming system performance and health.
-
Workshop 10.2:
Implementing alerting and notifications.
-
Workshop 10.3:
Utilizing monitoring tools and techniques.
-
Workshop 10.4:
Implementing streaming system maintenance strategies.
-
Workshop 10.5:
Best practices for monitoring.
-
Workshop 11.1:
Implementing event-driven architectures.
-
Workshop 11.2:
Utilizing change data capture (CDC).
-
Workshop 11.3:
Implementing stream processing for complex event processing (CEP).
-
Workshop 11.4:
Utilizing windowing and aggregation techniques.
-
Workshop 11.5:
Advanced techniques for real-time streaming.
-
Workshop 12.1:
Implementing real-time fraud detection systems.
-
Workshop 12.2:
Utilizing streaming data for IoT applications.
-
Workshop 12.3:
Implementing real-time analytics for e-commerce.
-
Workshop 12.4:
Utilizing streaming data for log analytics.
-
Workshop 12.5:
Best practices for real world applications.
-
Workshop 13.1:
Deploying streaming architectures on cloud platforms (AWS, Azure, GCP).
-
Workshop 13.2:
Utilizing cloud-based streaming services.
-
Workshop 13.3:
Optimizing cloud resources for streaming.
-
Workshop 13.4:
Best practices for cloud deployment.
-
Workshop 14.1:
Implementing data governance policies in streaming architectures.
-
Workshop 14.2:
Utilizing metadata management tools.
-
Workshop 14.3:
Implementing data lineage and data dictionary.
-
Workshop 14.4:
Best practices for data governance.
-
Workshop 15.1:
Emerging trends in real-time data streaming.
-
Workshop 15.2:
Utilizing AI and automation in streaming architectures.
-
Workshop 15.3:
Implementing edge streaming and processing.
-
Workshop 15.4:
Best practices for future streaming architecture
