Course Outline
Introduction to the Stratio Ecosystem
- Examination of Stratio's architecture and its core components
- The specific contributions of Rocket and Intelligence modules throughout the data lifecycle
- Accessing the platform and mastering navigation of the Stratio interface
Utilizing the Rocket Module
- Strategies for data ingestion and establishing data pipelines
- Establishing connections to data sources and setting up transformation configurations
- Employing PySpark for preprocessing activities within the Rocket environment
PySpark Fundamentals for Stratio Users
- Core PySpark data structures and essential operations
- Implementing control flow: utilization of for, while, and if/else constructs
- Authoring custom functions using def and their practical application
Advanced Rocket Applications with PySpark
- Handling streaming ingestion and real-time transformations
- Integrating loops and functions in both batch processing and real-time scenarios
- Optimizing performance within PySpark pipelines through best practices
Discovering the Intelligence Module
- Overview of data modeling and analytical capabilities
- Techniques for feature selection, transformation, and exploratory analysis
- The role of PySpark in generating custom analytics and actionable insights
Constructing Sophisticated Analytics Workflows
- Developing user-defined functions (UDFs) within the Intelligence module
- Applying conditional statements and loops to structure data logic
- Practical applications: segmentation, aggregation, and predictive modeling
Deployment and Team Collaboration
- Saving, exporting, and reusing established workflows
- Collaborative practices for teams working within Stratio
- Evaluating outputs and integrating results with downstream tools
Recap and Future Pathways
Requirements
- Proficiency in Python programming
- Familiarity with data analytics or big data processing principles
- Foundational understanding of Apache Spark and distributed computing concepts
Target Audience
- Data engineers managing Stratio-based platforms
- Analysts and developers utilizing the Rocket and Intelligence modules
- Technical teams adopting PySpark workflows within the Stratio ecosystem
Testimonials (3)
Learning that the QGIS and a tool that can used by other different professionals such land survey
Bame Duncan Koko - Bentel Technologies (Pty) Ltd
Course - QGIS for Geographic Information System
How to use open satellites data for real applications
Tshering Dorji - Druk Holding and Investments
Course - Advanced Geographic Information Systems (GIS)
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.
