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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
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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