- 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
Platform Engineering is the discipline that combines software development, systems operations, and architecture to create large-scale data platforms.
This instructor-led, live training (online or onsite) is aimed at intermediate-level software developers and data engineers who wish to build and maintain robust platforms for data-intensive applications.
By the end of this training, participants will be able to:
- Understand the challenges and solutions in platform engineering for big data.
- Design and implement scalable data processing pipelines.
- Utilize big data frameworks like Hadoop and Spark effectively.
- Develop real-time analytics solutions using streaming data.
- Orchestrate complex data workflows with tools like Apache Airflow.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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