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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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