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Course Outline

Introduction to the Stratio Platform

  • An overview of Stratio's architecture and core modules.
  • The function of Rocket and Intelligence within the broader data lifecycle.
  • Logging in and navigating the Stratio user interface.

Working with the Rocket Module

  • Data ingestion strategies and pipeline creation.
  • Linking data sources and configuring transformation rules.
  • Utilising PySpark for preprocessing tasks within Rocket.

PySpark Essentials for Stratio Users

  • Understanding PySpark data structures and core operations.
  • Implementing looping constructs: for, while, and if/else statements.
  • Writing and applying custom functions using the def keyword.

Advanced Usage of Rocket with PySpark

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

Exploring the Intelligence Module

  • An overview of data modeling and analytical features.
  • Techniques for feature selection, transformation, and exploration.
  • The role of PySpark in enabling custom analytics and generating insights.

Building Advanced Analytics Workflows

  • Developing user-defined functions (UDFs) within the Intelligence module.
  • Using conditionals and loops to define complex data logic.
  • Practical use cases including segmentation, aggregation, and prediction.

Deployment and Collaboration

  • Saving, exporting, and reusing established workflows.
  • Collaborating with team members on the Stratio platform.
  • Reviewing outputs and integrating results with downstream tools.

Summary and Next Steps

Requirements

  • Proficiency in Python programming.
  • A solid grasp of data analytics or big data processing concepts.
  • Foundational knowledge of Apache Spark and distributed computing principles.

Target Audience

  • Data engineers operating on Stratio-based platforms.
  • Analysts or developers utilising the Rocket and Intelligence modules.
  • Technical teams transitioning their workflows to PySpark within the Stratio ecosystem.
 14 Hours

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