Transform and Validate Real-Time Data Fast

Overview

Imagine you’re tasked with solving a complex challenge that demands both strategic thinking and hands-on expertise. How do you approach it confidently? In this course, you will be guided through essential concepts and practical applications, empowering you to tackle real-world problems effectively. This course equips you with in-depth knowledge, interactive exercises, and actionable skills designed for immediate impact in your field. By the end of this course, you will have developed a robust understanding of key principles, gained experience with proven strategies, and be prepared to implement solutions in dynamic environments. Learners should be familiar with basic Python, SQL, basic PySpark, data engineering fundamentals, streaming concepts, and data quality awareness. This course is designed for intermediate data engineers, analytics engineers, and BI professionals who want to build reliable real-time data pipelines with automated quality checks and executive-ready dashboards using Microsoft Fabric, PySpark, and Power BI. By the end of this course, you'll be ready to apply what you’ve learned to drive results and adapt to evolving challenges with confidence.

Syllabus

Data Transformation

Learn to parse, flatten, and reshape real-time data streams into analytics-ready tables. Explore nested clickstream data, explode arrays, and pivot by category for efficient downstream analytics.

Automated Quality & Governance

In this module, learners will explore how to automate data validation using PyDeequ. They will learn to define and apply data quality constraints, integrate validation seamlessly into CI/CD pipelines, and implement mechanisms to block merges when thresholds are not met. This hands-on module emphasizes building robust, automated systems that safeguard data integrity in production environments.

Real-Time Analytics Integration

This module guides learners through optimizing Microsoft Power BI dashboards with live data connections. It covers real-time data integration, performance strategies such as caching and incremental refresh, and visual design principles.