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.
Transform and Validate Real-Time Data Fast
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
Included with
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.
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
February 2026
Assessments
1 assignment
Taught in English
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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
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There are 3 modules in this course
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
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
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.
What's included
5 videos1 reading1 assignment2 peer reviews
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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
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