Big Data Algorithms for Engineering Course
This course teaches big data algorithms for real-time analytics, covering streaming architectures, scalable metrics like HyperLogLog and Top-K, event schema design for video data, and strategies for building cost-effective, reliable pipelines. Students will develop skills to support large-scale systems with precise, actionable insights through hands-on exercises and optimization techniques.

4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain expertise in essential big data algorithms and streaming systems through this practical course. Design event schemas, create scalable data ingestion pipelines, and select optimal processing engines for real-time analytics. Hands-on practice includes Top-K, HyperLogLog, sessionization, and error metrics, with focus on optimizing cost, reliability, and performance using practical pseudocode and patterns.
Elevify advantages
Develop skills
- Design efficient streaming data pipelines for low-latency, reliable workflows.
- Implement scalable metrics using HyperLogLog, Top-K, and session windows.
- Model video events with analytics-focused schemas and quality controls.
- Optimize big data costs through storage, compute, and partitioning tuning.
- Enhance pipeline resilience with fault tolerance, testing, and monitoring.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students are saying
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