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Course Outline
Foundations of Data-Intensive Platform Engineering
- Overview of data-intensive applications for government
- Key challenges in platform engineering for big data
- Architectural considerations for data processing
Data Modeling and Management
- Principles for scalable data modeling
- Evaluating data storage and optimization strategies
- Governing data lifecycle in distributed systems
Big Data Processing Frameworks
- Assessment of big data processing tools (Hadoop, Spark, Flink)
- Comparison of batch and stream processing models
- Implementation of robust big data processing pipelines
Real-Time Analytics Platforms
- Designing architectures for real-time analytics
- Evaluating stream processing engines (Kafka Streams, Apache Storm)
- Developing real-time dashboards and visualization capabilities
Data Pipeline Orchestration
- Workflow management utilizing Apache Airflow and similar tools
- Strategies for automating data pipelines to enhance efficiency
- Establishing monitoring and alerting mechanisms for data pipelines
Platform Security and Compliance
- Adherence to security best practices for data platforms
- Ensuring data privacy and regulatory compliance for government
- Implementation of secure data access controls
Performance Tuning and Optimization
- Techniques for optimizing data throughput and reducing latency
- Strategies for scaling data-intensive platforms
- Conducting performance benchmarking and continuous monitoring
Case Studies and Best Practices
- Analysis of successful data platform implementations
- Insights derived from industry leaders
- Evaluation of emerging trends in data-intensive platform engineering
Capstone Project
- Designing a platform solution for a data-intensive application
- Implementation of a prototype data processing pipeline
- Evaluation of platform performance and scalability
Summary and Future Directions
Requirements
- Proficiency in fundamental data structures and algorithms
- Practical experience with Java, Scala, or Python programming
- Working knowledge of database fundamentals and SQL
Target Audience
- Software developers
- Data engineers
- Technical leads
21 Hours
Testimonials (2)
the overall concept of DevOps/DevSecOps is not a team but a culture, a mind set
Mpho Mashele - South Africa Reserve Bank
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