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Course Outline
Introduction to the Stratio Platform
- Examination of Stratio architecture and foundational components
- The function of Rocket and Intelligence within the data lifecycle for government operations
- Authentication protocols and navigation of the Stratio user interface
Utilizing the Rocket Module
- Data ingestion procedures and pipeline development
- Establishing connections to data sources and configuring transformation rules
- Implementation of PySpark for preprocessing tasks within the Rocket environment
Fundamental PySpark Concepts for Stratio Users
- Overview of PySpark data structures and standard operations
- Application of iterative constructs: for, while, and conditional logic
- Development and application of custom functions using def statements
Advanced PySpark Integration with Rocket
- Real-time data streaming ingestion and transformation capabilities
- Deployment of loops and functions in both batch processing and real-time contexts
- Adherence to best practices for optimizing PySpark pipeline performance
Investigating the Intelligence Module
- Overview of data modeling and analytical features
- Procedures for feature selection, transformation, and exploratory analysis
- The contribution of PySpark to custom analytics and actionable insights for government decision-making
Developing Advanced Analytics Workflows
- Creation of user-defined functions (UDFs) within the Intelligence module
- Application of conditional logic and loops to enforce data governance rules
- Relevant use cases: segmentation, aggregation, and predictive modeling for public sector applications
Deployment and Collaborative Governance
- Procedures for saving, exporting, and reusing analytic workflows
- Facilitating collaboration among team members within the Stratio environment
- Validation of outputs and integration with downstream systems for government accountability
Summary and Next Steps
Requirements
- Demonstrated proficiency in Python development
- Fundamental comprehension of data analytics and large-scale data processing methodologies
- Foundational understanding of Apache Spark architecture and distributed computing principles
Audience
- Data engineers managing infrastructure on Stratio-based environments
- Analysts and developers utilizing Rocket and Intelligence modules
- Technical personnel migrating to PySpark workflows within the Stratio ecosystem for government applications
14 Hours
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Equipped with examples