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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

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