Process & Analyze Real-Time Data Fast
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Process & Analyze Real-Time Data Fast
This course is part of Real-Time, Real Fast: Kafka & Spark for Data Engineers Specialization
Instructors: Jairo Sanchez
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What you'll learn
Architect a streaming data solution by differentiating between batch, micro-batch, and streaming patterns to solve a specific business problem.
Develop real-time analytics pipelines using window functions and watermarking to aggregate and analyze streaming data.
Optimize a production streaming application by diagnosing performance bottlenecks like data skew and implementing mitigation techniques.
Skills you'll gain
Tools you'll learn
Details to know
February 2026
1 assignment
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There are 3 modules in this course
In a world where business decisions happen in seconds, is your data fast enough? Traditional batch processing creates a critical "insight lag," forcing you to react to yesterday's news. This hands-on course empowers you to design, build, and optimize high-speed data pipelines that serve as the nervous system of modern business. Working in a ready-to-use cloud environment with industry-standard Apache Spark, you will master the complete lifecycle of real-time data engineering. Through practical, real-world case studies from e-commerce, IoT, and FinTech, you'll learn to build live operational dashboards, apply window functions to analyze trends over time, and design a sophisticated, real-time fraud detection engine. You will leave this course with the skills to transform massive, high-speed data streams into immediate, actionable business value and become the go-to expert for creating low-latency solutions that give companies their competitive edge.
This course is designed for professionals and aspiring practitioners who want to harness the power of real-time analytics. Whether you are a data analyst, data engineer, or data scientist seeking to advance your skills, or an IT professional and developer working with IoT, cloud, or streaming systems, this course will equip you with the practical tools and techniques to analyze data as it flows. Business professionals will also benefit from understanding how real-time insights can accelerate smarter decision-making across industries. Learners should have a basic understanding of Python and SQL to follow the exercises effectively. The hands-on labs use a free Databricks account, and a setup guide is provided, so no prior experience with Databricks or Spark is required to get started. By the end of this course, learners will be able to design and implement efficient real-time data solutions using streaming technologies. They will learn to differentiate between batch, micro-batch, and continuous streaming patterns to solve business problems, apply time-based functions and watermarking for stateful data analysis, and optimize streaming pipelines by identifying and resolving performance challenges such as data skew.
In this module, learner will step into the role of a data analyst at a fast-growing e-commerce company. Learner will tackle their biggest challenge: replacing slow, nightly reports with a live dashboard to monitor a critical flash sale. Learner will master the fundamentals of stream processing and learn why real-time data is a competitive necessity. This module demonstrates these concepts using Apache Spark.
What's included
4 videos2 readings1 peer review
4 videos•Total 35 minutes
- The Real-Time Revolution•4 minutes
- Data Processing Paradigms•8 minutes
- Introduction to Spark Structured Streaming•10 minutes
- Your First Streaming Pipeline•14 minutes
2 readings•Total 10 minutes
- Welcome to the Course: Course Overview•5 minutes
- Key Streaming Terminologies for Real-Time Analytics•5 minutes
1 peer review•Total 30 minutes
- Hands-On-Learning: Building a Real-Time E-commerce Dashboard •30 minutes
As an IoT Engineer for a smart city initiative, you are responsible for making sense of hundreds of traffic sensors generating chaotic, often delayed data. In this module, you will explore the critical distinction between event time and processing time, master stateful analytics using window functions, and apply watermarking to handle late-arriving data. By the end, you'll be able to design robust real-time pipelines that reveal trends and actionable insights from complex, continuous streams.
What's included
3 videos1 reading1 peer review
3 videos•Total 36 minutes
- The Power of State and Time•10 minutes
- Deep Dive on Tumbling and Sliding Windows•13 minutes
- Implementing Session Windows and Watermarking•13 minutes
1 reading•Total 5 minutes
- Managing Chaos with Watermarking•5 minutes
1 peer review•Total 30 minutes
- Hands-On-Learning: Analyzing IoT Network Trends•30 minutes
This module will guide you through identifying and resolving performance bottlenecks using techniques like salting, and then applying stateful analytics to build a prototype for real-time fraud detection. In your role as a Platform Engineer at a fast-growing FinTech company, you are challenged with stabilizing a critical payment pipeline crippled by data skew and tasked with defending against rapidly evolving fraud threats. By the end, you will have mastered the skills needed to optimize and operationalize production-grade streaming applications in high-stakes environments.
What's included
4 videos1 reading1 assignment2 peer reviews
4 videos•Total 36 minutes
- Understanding and Diagnosing Data Skew•9 minutes
- Implementing Salting•10 minutes
- Applying Streaming to Fraud Detection•12 minutes
- Congratulations and Next Steps in Your Data Engineering Career•5 minutes
1 reading•Total 5 minutes
- The Salting Technique•5 minutes
1 assignment•Total 20 minutes
- Process & Analyze Real-Time Data Fast •20 minutes
2 peer reviews•Total 90 minutes
- Hands-On-Learning: Diagnosing and Mitigating Data Skew in a Payment Pipeline•30 minutes
- Project: Build a Real-Time Fraud Detection Pipeline•60 minutes
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