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Course Outline

Foundations of Data-Intensive Platform Engineering

  • Introduction to data-intensive application architectures
  • Key challenges in big data platform engineering
  • Overview of data processing architectural patterns

Data Modeling and Management

  • Principles for scalable data modeling
  • Evaluating data storage options and optimization strategies
  • Managing the data lifecycle within distributed systems

Big Data Processing Frameworks

  • Survey of major processing tools (Hadoop, Spark, Flink)
  • Distinctions between batch and stream processing
  • Constructing end-to-end big data processing pipelines

Real-Time Analytics Platforms

  • Architecting systems for real-time analytics
  • Stream processing engines (Kafka Streams, Apache Storm)
  • Creating real-time dashboards and visual interfaces

Data Pipeline Orchestration

  • Workflow management using Apache Airflow and similar tools
  • Automating pipelines to enhance operational efficiency
  • Implementing monitoring and alerting mechanisms for pipelines

Platform Security and Compliance

  • Best practices for securing data platforms
  • Ensuring data privacy and regulatory adherence
  • Deploying secure data access control mechanisms

Performance Tuning and Optimization

  • Strategies for optimizing data throughput and reducing latency
  • Scaling approaches for data-intensive environments
  • Conducting performance benchmarking and continuous monitoring

Case Studies and Best Practices

  • Analyzing successful data platform implementations
  • Insights and lessons from industry leaders
  • Exploring emerging trends in platform engineering

Capstone Project

  • Designing a comprehensive platform solution for a data-intensive use case
  • Building a functional prototype of the data processing pipeline
  • Evaluating the platform’s performance and scalability

Summary and Next Steps

Requirements

  • Solid grasp of fundamental data structures and algorithms
  • Practical experience with Java, Scala, or Python
  • Knowledge of core database concepts and SQL

Target Audience

  • Software developers
  • Data engineers
  • Technical leads
 21 Hours

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