Data Engineering For Real-time Analytics Training Course: Build Real-time Systems

Introduction

Harness the power of instant insights with our Data Engineering for Real-Time Analytics Training Course. This program is designed to equip you with the essential skills to build systems that can do analytical processing of real-time data, enabling you to deliver actionable insights as they happen. In today's fast-paced data landscape, mastering real-time analytics is crucial for organizations seeking to gain a competitive edge. Our real-time analytics training course offers hands-on experience and expert guidance, empowering you to design and implement high-performance real-time data solutions.

This build real-time systems training delves into the core concepts of data engineering for real-time analytics, covering topics such as stream processing, event-driven architectures, and real-time data warehousing. You'll gain expertise in using industry-standard tools and techniques to build systems that can do analytical processing of real-time data, meeting the demands of modern data-intensive environments. Whether you're a data engineer, data scientist, or systems architect, this Data Engineering for Real-Time Analytics course will empower you to build and maintain efficient and scalable real-time data systems.

Target Audience:

• Data Engineers
• Data Scientists
• Systems Architects
• Backend Developers
• Cloud Engineers
• Data Analysts
• Anyone needing real-time analytics skills

Course Objectives:

• Understand the fundamentals of data engineering for real-time analytics.
• Master stream processing and event-driven architectures.
• Utilize real-time data warehousing and analytics tools.
• Implement data ingestion and transformation for real-time data.
• Design and build scalable real-time data pipelines.
• Optimize real-time data systems for low latency and high throughput.
• Troubleshoot and address common issues in real-time data implementations.
• Implement data governance and security for real-time data.
• Integrate real-time data systems with various data sources and applications.
• Understand how to handle large-scale real-time data processing.
• Explore advanced real-time analytics patterns (e.g., complex event processing, real-time machine learning).
• Apply real world use cases for data engineering in real-time analytics.
• Leverage real-time analytics tools and frameworks for efficient development.

Duration

10 Days

Course content

Module 1: Introduction to Real-Time Analytics

• Fundamentals of data engineering for real-time analytics.
• Overview of stream processing, event-driven architectures, and real-time data warehousing.
• Setting up a real-time analytics development environment.
• Introduction to real-time analytics tools and frameworks.
• Best practices for real-time data engineering.

Module 2: Stream Processing and Event-Driven Architectures

• Mastering stream processing and event-driven architectures.
• Utilizing stream processing frameworks (Apache Kafka, Flink, Spark Streaming).
• Implementing event-driven data pipelines.
• Designing and building real-time data streams.
• Best practices for stream processing.

Module 3: Real-Time Data Warehousing and Analytics

• Utilizing real-time data warehousing and analytics tools.
• Implementing real-time OLAP and data aggregation.
• Designing and building real-time dashboards and reports.
• Optimizing data warehousing for real-time queries.
• Best practices for real-time data warehousing.

Module 4: Data Ingestion and Transformation

• Implementing data ingestion and transformation for real-time data.
• Utilizing message queues and data ingestion tools.
• Designing and building real-time data transformation pipelines.
• Optimizing data transformation for low latency.
• Best practices for data ingestion.

Module 5: Scalable Real-Time Data Pipelines

• Designing and building scalable real-time data pipelines.
• Utilizing cloud-based real-time data processing.
• Implementing microservices architecture for real-time systems.
• Designing scalable real-time data architectures.
• Best practices for pipeline architecture.

Module 6: Real-Time System Optimization

• Optimizing real-time data systems for low latency and high throughput.
• Utilizing performance tuning and resource management.
• Implementing data compression and caching.
• Designing efficient real-time data applications.
• Best practices for optimization.

Module 7: Troubleshooting Real-Time Implementations

• Troubleshooting and addressing common issues in real-time data implementations.
• Analyzing real-time data logs and error messages.
• Utilizing problem-solving techniques for resolution.
• Resolving common real-time data errors.
• Best practices for troubleshooting.

Module 8: Data Governance and Security

• Implementing data governance and security for real-time data.
• Utilizing data access control and audit logging.
• Designing and building secure real-time data systems.
• Optimizing security for real-time data protection.
• Best practices for governance.

Module 9: Integration with Data Systems

• Integrating real-time data systems with various data sources and applications.
• Utilizing APIs and data connectors.
• Implementing real-time data integration with other systems.
• Designing efficient data integration strategies.
• Best practices for integration.

Module 10: Large-Scale Real-Time Processing

• Understanding how to handle large-scale real-time data processing.
• Utilizing distributed real-time data processing frameworks.
• Implementing data sharding and parallel processing.
• Designing scalable real-time data processing solutions.
• Best practices for large scale data.

Module 11: Advanced Real-Time Analytics Patterns

• Exploring advanced real-time analytics patterns (complex event processing, real-time machine learning).
• Utilizing complex event processing (CEP) for real-time pattern detection.
• Implementing real-time machine learning for predictive analytics.
• Designing and building advanced real-time data solutions.
• Optimizing advanced patterns for specific applications.
• Best practices for advanced patterns.

Module 12: Real-World Use Cases

• Implementing real-time analytics for financial transaction monitoring.
• Utilizing real-time analytics for e-commerce personalization.
• Implementing real-time analytics for IoT data streams.
• Utilizing real-time analytics for social media monitoring.
• Best practices for real-world applications.

Module 13: Real-Time Tools Implementation

• Utilizing real-time analytics tools and frameworks (Kafka Streams, KSQL, Apache Beam).
• Implementing real-time data processing with specific tools.
• Designing and building automated real-time workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Monitoring and Metrics

• Implementing monitoring and metrics for real-time data systems.
• Utilizing real-time data monitoring tools.
• Designing and building real-time dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in Real-Time Analytics

• Emerging trends in data engineering for real-time analytics.
• Utilizing AI for real-time data processing.
• Implementing serverless real-time data pipelines.
• Best practices for future applications.

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.