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 Duration 21 hours

Course Outline

Core Principles of Data-Intensive Platform Engineering

  • Introduction to the nature of data-intensive applications
  • Key challenges in engineering platforms for big data
  • Broad overview of data processing architectures

Data Modeling and Governance

  • Scalability-focused data modeling principles
  • Exploring data storage options and optimization strategies
  • Managing the data lifecycle across distributed systems

Major Big Data Processing Frameworks

  • Survey of essential processing tools including Hadoop, Spark, and Flink
  • Distinguishing between batch and stream processing
  • Establishing a functional big data processing pipeline

Real-Time Analytics Infrastructure

  • Designing architectures for real-time analytics
  • Exploring stream processing engines such as Kafka Streams and Apache Storm
  • Constructing real-time dashboards and data visualizations

Orchestrating Data Pipelines

  • Workflow management using Apache Airflow and similar tools
  • Automation strategies for pipeline efficiency
  • Implementing monitoring and alerting mechanisms

Security and Compliance for Platforms

  • Best practices for securing data platforms
  • Maintaining data privacy and regulatory compliance
  • Establishing robust data access controls

Performance Tuning and Efficiency

  • Methods for optimizing data throughput and reducing latency
  • Strategies for scaling data-intensive platforms
  • Conducting performance benchmarking and continuous monitoring

Industry Case Studies and Best Practices

  • Analyzing successful implementations of data platforms
  • Extracting lessons from industry leaders
  • Examining emerging trends in platform engineering

Capstone Project

  • Architecting a comprehensive solution for a data-intensive application
  • Developing a working prototype of the data processing pipeline
  • Assessing the platform's performance and scalability

Wrap-up and Future Directions

Requirements

  • Solid grasp of fundamental data structures and algorithms
  • Practical experience with Java, Scala, or Python
  • Working knowledge of database fundamentals and SQL

Target Audience

  • Software developers
  • Data engineers
  • Technical leads

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