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 Duration 14 hours

Course Outline

Introduction to the Stratio Platform

  • Summary of Stratio architecture and fundamental components
  • Function of Rocket and Intelligence modules within the data management lifecycle
  • Procedures for authentication and interface navigation in the Stratio user interface

Operations using the Rocket Module

  • Data acquisition and pipeline construction
  • Establishment of data source connections and transformation configuration
  • Application of PySpark for preprocessing functions within Rocket

PySpark Fundamentals for Stratio Users

  • PySpark data structures and operational methods
  • Iterative logic: implementation of for, while, and conditional branching
  • Development and application of custom functions using def

Advanced Application of Rocket with PySpark

  • Stream data ingestion and real-time transformations
  • Utilization of iterative logic and functions in batch processing and live environments
  • Recommended practices for optimizing performance in PySpark pipelines

Examination of the Intelligence Module

  • Summary of data modeling and analytical capabilities
  • Selection, transformation, and exploration of data features
  • Application of PySpark in custom analytical processes and insight generation

Development of Complex Analytical Workflows

  • Creation of user-defined functions (UDFs) within the Intelligence module
  • Implementation of conditional logic and iterative structures for data processing rules
  • Practical applications: data segmentation, aggregation, and predictive modeling

Deployment and Collaborative Practices

  • Saving, exporting, and repurposing workflow configurations
  • Coordination and collaboration with personnel using Stratio
  • Evaluation of output and integration with downstream systems

Conclusion and Future Actions

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 responsible for Stratio-based platform operations
  • Analysts and developers utilizing Rocket and Intelligence modules
  • Technical teams adopting PySpark workflows within the Stratio environment

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