Stratio: Rocket and Intelligence Modules with PySpark

Prerequisites:

• Experience with Python programming
• Understanding of data analytics or big data processing concepts
• Basic knowledge of Apache Spark and distributed computing

Audience

• Data engineers working on Stratio-based platforms
• Analysts or developers using Rocket and Intelligence modules
• Technical teams transitioning to PySpark workflows within Stratio

Overview:

Stratio is a data-centric platform that integrates big data, AI, and governance into a single solution. Its Rocket and Intelligence modules enable rapid data exploration, transformation, and advanced analytics in enterprise environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

By the end of this training, participants will be able to:

• Navigate and work within the Stratio platform using Rocket and Intelligence modules.
• Apply PySpark in the context of data ingestion, transformation, and analysis.
• Use loops and conditional logic to control data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline:

Introduction to Stratio Platform

• Overview of Stratio architecture and core modules
• Role of Rocket and Intelligence in the data lifecycle
• Logging in and navigating the Stratio UI

Working with the Rocket Module

• Data ingestion and pipeline creation
• Connecting data sources and configuring transformations
• Using PySpark for preprocessing tasks in Rocket

PySpark Essentials for Stratio Users

• PySpark data structures and operations
• Looping constructs: for, while, if/else usage
• Writing custom functions with def and applying them

Advanced Usage of Rocket with PySpark

• Streaming ingestion and transformations
• Using loops and functions in batch and real-time scenarios
• Best practices for performance in PySpark pipelines

Exploring the Intelligence Module

• Overview of data modeling and analysis features
• Feature selection, transformation, and exploration
• Role of PySpark in custom analytics and insights

Building Advanced Analytics Workflows

• Creating user-defined functions (UDFs) in Intelligence
• Applying conditionals and loops for data logic
• Use cases: segmentation, aggregation, and prediction

Deployment and Collaboration

• Saving, exporting, and reusing workflows
• Collaborating with other team members on Stratio
• Reviewing output and integrating with downstream tools

Summary and Next Steps