Plan Szkolenia

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

Wymagania

  • An understanding of basic data structures and algorithms
  • Experience with Java, Scala, or Python programming
  • Familiarity with basic concepts of databases and SQL

Audience

  • Software developers
  • Data engineers
  • Technical leads
 21 godzin

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Opinie uczestników (4)

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