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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 the 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
- A solid understanding of basic data structures and algorithms.
- Practical experience with Java, Scala, or Python programming languages.
- Familiarity with fundamental 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
