Course Outline
Introduction to the Stratio Platform
- Examination of Stratio’s architecture and its core modules.
- The specific roles played by Rocket and Intelligence throughout the data lifecycle.
- Procedures for logging in and navigating the Stratio user interface.
Utilizing the Rocket Module
- Processes for data ingestion and the creation of data pipelines.
- Establishing connections to data sources and setting up transformations.
- Leveraging PySpark for preprocessing tasks within the Rocket module.
PySpark Fundamentals for Stratio Users
- Core PySpark data structures and common operations.
- Implementing looping constructs, including for loops, while loops, and if/else statements.
- Defining and applying custom functions using the def keyword.
Advanced Rocket Integration with PySpark
- Handling streaming ingestion and real-time transformations.
- Applying loops and functions in both batch processing and real-time scenarios.
- Adhering to best practices for optimizing PySpark pipeline performance.
Discovering the Intelligence Module
- An overview of available data modeling and analytical features.
- Techniques for feature selection, transformation, and exploratory analysis.
- The contribution of PySpark to custom analytics and insight generation.
Constructing Sophisticated Analytics Workflows
- Development of user-defined functions (UDFs) within the Intelligence module.
- Utilizing conditionals and loops to control complex data logic.
- Practical applications, including segmentation, aggregation, and predictive modeling.
Deployment and Team Collaboration
- Strategies for saving, exporting, and reusing established workflows.
- Methods for collaborating with other team members on the Stratio platform.
- Reviewing outputs and integrating results with downstream tools.
Recap and Future Directions
Requirements
- Practical experience with Python programming.
- A solid grasp of data analytics or big data processing principles.
- Foundational knowledge of Apache Spark and distributed computing concepts.
Target Audience
- Data engineers developing or maintaining applications on Stratio-based platforms.
- Analysts or developers utilizing the Rocket and Intelligence modules.
- Technical teams transitioning their workflows to PySpark 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.
