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 Duration 14 hours

Course Outline

Introduction to the Stratio Platform

  • Overview of Stratio’s architecture and core modules
  • The role of Rocket and Intelligence in the data lifecycle
  • Accessing and navigating the Stratio user interface

Utilizing the Rocket Module

  • Establishing data ingestion pipelines
  • Linking data sources and configuring transformation rules
  • Employing PySpark for preprocessing tasks within Rocket

PySpark Fundamentals for Stratio Users

  • Exploring PySpark data structures and operations
  • Implementing looping constructs: for, while, and if/else statements
  • Defining and applying custom functions using def

Advanced Rocket Integration with PySpark

  • Managing streaming ingestion and transformations
  • Applying loops and functions in both batch and real-time contexts
  • Best practices for optimizing PySpark pipeline performance

Insights from the Intelligence Module

  • Overview of data modeling and analytical features
  • Techniques for feature selection, transformation, and exploration
  • The contribution of PySpark to custom analytics and insights

Constructing Advanced Analytics Workflows

  • Creating user-defined functions (UDFs) within Intelligence
  • Implementing conditionals and loops for complex data logic
  • Practical applications: segmentation, aggregation, and prediction

Deployment and Team Collaboration

  • Saving, exporting, and reusing workflow components
  • Collaborating effectively with team members on Stratio
  • Reviewing outputs and integrating with downstream tools

Recap and Future Directions

Requirements

  • Proficiency in Python programming
  • Familiarity with data analytics or big data processing concepts
  • Fundamental understanding of Apache Spark and distributed computing principles

Target Audience

  • Data engineers operating within Stratio-based platforms
  • Analysts and developers leveraging the Rocket and Intelligence modules
  • Technical teams adopting PySpark workflows within the Stratio ecosystem

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