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 Duration 14 hours

Course Outline

Getting to Know the Stratio Platform

  • Overview of Stratio's architecture and primary modules
  • How Rocket and Intelligence modules fit into the data lifecycle
  • Accessing and moving around the Stratio user interface

Using the Rocket Module

  • Importing data and building pipelines
  • Linking data sources and setting up transformations
  • Leveraging PySpark for pre-processing within Rocket

Key PySpark Concepts for Stratio Users

  • Core PySpark data structures and operations
  • Looping syntax: using for, while, and if/else
  • Defining and utilizing custom functions with def

Advanced Rocket Implementation with PySpark

  • Real-time data ingestion and transformation
  • Implementing loops and functions in batch and live scenarios
  • Performance optimization techniques for PySpark pipelines

Discovering the Intelligence Module

  • Features for data modelling and analysis
  • Selecting, transforming, and exploring features
  • The part PySpark plays in custom analytics and insights

Creating Complex Analytics Workflows

  • Developing user-defined functions (UDFs) in Intelligence
  • Applying conditionals and loops for data logic
  • Practical applications: segmentation, aggregation, and forecasting

Deployment and Team Collaboration

  • Storing, exporting, and reusing workflows
  • Collaborating with colleagues on the Stratio platform
  • Checking outputs and connecting with downstream tools

Recap and Future Steps

Requirements

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

Target Audience

  • Data engineers working on Stratio-based platforms
  • Analysts or developers leveraging Rocket and Intelligence modules
  • Technical teams shifting towards PySpark workflows within Stratio

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Testimonials (3)

Provisional Upcoming Courses (Require 5+ participants)

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