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 Duration 14 hours

Course Outline

Introduction to the Stratio Platform

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

Utilizing the Rocket Module

  • Data ingestion strategies and pipeline construction
  • Linking data sources and setting up transformations
  • Applying PySpark for preprocessing tasks within Rocket

PySpark Fundamentals for Stratio Users

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

Advanced Rocket Applications with PySpark

  • Streaming ingestion and real-time transformations
  • Integrating loops and functions in batch and real-time scenarios
  • Best practices for optimizing PySpark pipeline performance

Discovering the Intelligence Module

  • Overview of data modeling and analytical features
  • Feature selection, transformation, and exploratory analysis
  • Leveraging PySpark for custom analytics and insight generation

Constructing Sophisticated Analytics Workflows

  • Building user-defined functions (UDFs) within Intelligence
  • Employing conditionals and loops for complex data logic
  • Practical use cases: segmentation, aggregation, and predictive modeling

Deployment and Team Collaboration

  • Saving, exporting, and reusing established workflows
  • Collaborative workflows with team members on Stratio
  • Validating outputs and integrating with downstream systems

Conclusion and Future Steps

Requirements

  • Proficiency in Python programming
  • Familiarity with data analytics or big data processing concepts
  • Foundational knowledge of Apache Spark and distributed computing

Target Audience

  • Data engineers operating within Stratio-based ecosystems
  • Analysts or developers utilizing Rocket and Intelligence modules
  • Technical teams adopting PySpark workflows within Stratio

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