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Course Outline
Introduction to Stratio Platform
- Overview of Stratio architecture and core modules
- Role of Rocket and Intelligence in the data lifecycle
- Logging in and navigating the Stratio UI
Working with the Rocket Module
- Data ingestion and pipeline creation
- Connecting data sources and configuring transformations
- Using PySpark for preprocessing tasks in Rocket
PySpark Essentials for Stratio Users
- PySpark data structures and operations
- Looping constructs: for, while, if/else usage
- Writing custom functions with def and applying them
Advanced Usage of Rocket with PySpark
- Streaming ingestion and transformations
- Using loops and functions in batch and real-time scenarios
- Best practices for performance in PySpark pipelines
Exploring the Intelligence Module
- Overview of data modeling and analysis features
- Feature selection, transformation, and exploration
- Role of PySpark in custom analytics and insights
Building Advanced Analytics Workflows
- Creating user-defined functions (UDFs) in Intelligence
- Applying conditionals and loops for data logic
- Use cases: segmentation, aggregation, and prediction
Deployment and Collaboration
- Saving, exporting, and reusing workflows
- Collaborating with other team members on Stratio
- Reviewing output and integrating with downstream tools
Summary and Next Steps
Requirements
- Experience with Python programming
- Understanding of data analytics or big data processing concepts
- Basic knowledge of Apache Spark and distributed computing
Audience
- Data engineers working on Stratio-based platforms
- Analysts or developers using Rocket and Intelligence modules
- Technical teams transitioning to PySpark workflows within Stratio
14 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
