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Course Outline
Foundations of Data-Intensive Platform Engineering
- Introduction to data-intensive applications
- Challenges in platform engineering for big data
- Overview of data processing architectures
Data Modeling and Management
- Principles of data modeling for scalability
- Data storage options and optimization
- Managing data lifecycle in a distributed environment
Big Data Processing Frameworks
- Overview of big data processing tools (Hadoop, Spark, Flink)
- Batch vs. stream processing
- Setting up a big data processing pipeline
Real-Time Analytics Platforms
- Architecting for real-time analytics
- Stream processing engines (Kafka Streams, Apache Storm)
- Building real-time dashboards and visualizations
Data Pipeline Orchestration
- Workflow management with Apache Airflow and others
- Automating data pipelines for efficiency
- Monitoring and alerting for data pipelines
Platform Security and Compliance
- Security best practices for data platforms
- Ensuring data privacy and regulatory compliance
- Implementing secure data access controls
Performance Tuning and Optimization
- Techniques for optimizing data throughput and latency
- Scaling strategies for data-intensive platforms
- Performance benchmarking and monitoring
Case Studies and Best Practices
- Analyzing successful data platform implementations
- Lessons learned from industry leaders
- Emerging trends in data-intensive platform engineering
Capstone Project
- Designing a platform solution for a data-intensive application
- Implementing a prototype of the data processing pipeline
- Evaluating the platform's performance and scalability
Summary and Next Steps
Requirements
- Familiarity with fundamental data structures and algorithms.
- Programming experience in Java, Scala, or Python.
- Understanding of basic database concepts and SQL.
Audience
- Software developers.
- Data engineers.
- Technical leads.
21 Hours
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About the microservices and how to maintenance kubernetes
