Transform and Validate Real-Time Data Fast

This course is part of Real-Time, Real Fast: Kafka & Spark for Data Engineers Specialization

Instructor: Tom Themeles

Access provided by Nielsen

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Transform nested and streaming data into analytics-ready tables using programming tools and platforms.
Implement automated data quality checks and integrate these checks into CI/CD pipelines to enforce quality gates.
Build and manage scalable real-time analytics pipelines that block low-quality data and connect curated datasets to Power BI dashboards.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Real-Time, Real Fast: Kafka & Spark for Data Engineers Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

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.

What's included

4 videos2 readings1 peer review

4 videos Total 32 minutes

Welcome to the Course 5 minutes
Introduction to Real-Time Data 6 minutes
Parsing and Exploding Nested JSON 11 minutes
Pivoting for Analytics 11 minutes

2 readings Total 15 minutes

Welcome to the Course: Transform and Validate Real-Time Data Fast 5 minutes
Real Time Intelligence in Microsoft Fabric 10 minutes

1 peer review Total 20 minutes

Hands-On-Learning: Transform Data into Analytics-Ready Tables 20 minutes

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.

What's included

4 videos1 reading1 peer review

4 videos Total 37 minutes

Benefits of Using PySpark 6 minutes
Integration of Deequ in CI/CD Workflow 11 minutes
Automating Data Quality Checks with CI/CD in Microsoft Fabric 10 minutes
Blocking Low-Quality Merges 11 minutes

1 reading Total 5 minutes

Optimizing for CI/CD in Microsoft Fabric 5 minutes

1 peer review Total 30 minutes

Hands-On-Learning: Automate Data Quality Validation with PySpark and CI/CD Integration 30 minutes

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.