15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Data Scientists
- Systems Architects
- Backend Developers
- Cloud Engineers
- Data Analysts
- Anyone needing real-time analytics skills
Session 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.
About the Course
Power instant enterprise decisions with our Data Engineering for Real-Time Analytics Training Course. Tailored for data engineers, system architects, and senior analytics professionals, this hands-on program equips technical teams to architect, build, and scale low-latency data pipelines. Guided by streaming architecture experts, you will master event-driven design, real-time stream processing engines, stateful event aggregation, and modern real-time data warehousing—enabling your enterprise to capture continuous data streams, eliminate ingestion lag, and deliver sub-second insights at scale.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data engineering for real-time analytics.
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Workshop 1.2:
Overview of stream processing, event-driven architectures, and real-time data warehousing.
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Workshop 1.3:
Setting up a real-time analytics development environment.
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Workshop 1.4:
Introduction to real-time analytics tools and frameworks.
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Workshop 1.5:
Best practices for real-time data engineering.
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Workshop 2.1:
Mastering stream processing and event-driven architectures.
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Workshop 2.2:
Utilizing stream processing frameworks (Apache Kafka, Flink, Spark Streaming).
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Workshop 2.3:
Implementing event-driven data pipelines.
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Workshop 2.4:
Designing and building real-time data streams.
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Workshop 2.5:
Best practices for stream processing.
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Workshop 3.1:
Utilizing real-time data warehousing and analytics tools.
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Workshop 3.2:
Implementing real-time OLAP and data aggregation.
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Workshop 3.3:
Designing and building real-time dashboards and reports.
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Workshop 3.4:
Optimizing data warehousing for real-time queries.
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Workshop 3.5:
Best practices for real-time data warehousing.
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Workshop 4.1:
Implementing data ingestion and transformation for real-time data.
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Workshop 4.2:
Utilizing message queues and data ingestion tools.
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Workshop 4.3:
Designing and building real-time data transformation pipelines.
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Workshop 4.4:
Optimizing data transformation for low latency.
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Workshop 4.5:
Best practices for data ingestion.
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Workshop 5.1:
Designing and building scalable real-time data pipelines.
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Workshop 5.2:
Utilizing cloud-based real-time data processing.
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Workshop 5.3:
Implementing microservices architecture for real-time systems.
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Workshop 5.4:
Designing scalable real-time data architectures.
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Workshop 5.5:
Best practices for pipeline architecture.
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Workshop 6.1:
Optimizing real-time data systems for low latency and high throughput.
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Workshop 6.2:
Utilizing performance tuning and resource management.
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Workshop 6.3:
Implementing data compression and caching.
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Workshop 6.4:
Designing efficient real-time data applications.
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Workshop 6.5:
Best practices for optimization.
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Workshop 7.1:
Troubleshooting and addressing common issues in real-time data implementations.
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Workshop 7.2:
Analyzing real-time data logs and error messages.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common real-time data errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data governance and security for real-time data.
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Workshop 8.2:
Utilizing data access control and audit logging.
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Workshop 8.3:
Designing and building secure real-time data systems.
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Workshop 8.4:
Optimizing security for real-time data protection.
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Workshop 8.5:
Best practices for governance.
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Workshop 9.1:
Integrating real-time data systems with various data sources and applications.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing real-time data integration with other systems.
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Workshop 9.4:
Designing efficient data integration strategies.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle large-scale real-time data processing.
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Workshop 10.2:
Utilizing distributed real-time data processing frameworks.
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Workshop 10.3:
Implementing data sharding and parallel processing.
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Workshop 10.4:
Designing scalable real-time data processing solutions.
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Workshop 10.5:
Best practices for large scale data.
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Workshop 11.1:
Exploring advanced real-time analytics patterns (complex event processing, real-time machine learning).
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Workshop 11.2:
Utilizing complex event processing (CEP) for real-time pattern detection.
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Workshop 11.3:
Implementing real-time machine learning for predictive analytics.
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Workshop 11.4:
Designing and building advanced real-time data solutions.
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Workshop 11.5:
Optimizing advanced patterns for specific applications.
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Workshop 11.6:
Best practices for advanced patterns.
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Workshop 12.1:
Implementing real-time analytics for financial transaction monitoring.
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Workshop 12.2:
Utilizing real-time analytics for e-commerce personalization.
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Workshop 12.3:
Implementing real-time analytics for IoT data streams.
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Workshop 12.4:
Utilizing real-time analytics for social media monitoring.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing real-time analytics tools and frameworks (Kafka Streams, KSQL, Apache Beam).
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Workshop 13.2:
Implementing real-time data processing with specific tools.
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Workshop 13.3:
Designing and building automated real-time workflows.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing monitoring and metrics for real-time data systems.
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Workshop 14.2:
Utilizing real-time data monitoring tools.
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Workshop 14.3:
Designing and building real-time dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in data engineering for real-time analytics.
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Workshop 15.2:
Utilizing AI for real-time data processing.
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Workshop 15.3:
Implementing serverless real-time data pipelines.
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Workshop 15.4:
Best practices for future applications.
