Big Data Algorithms for Engineering Course
This course teaches big data algorithms for real-time analytics in engineering. Students learn streaming architectures, scalable metrics such as HyperLogLog and Top-K, event modeling for video platforms, and strategies to build cost-efficient, reliable pipelines. It covers designing ingestion systems, processing engines, sessionization, error metrics, and optimization techniques using practical pseudocode for large-scale tech systems delivering accurate insights.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in big data algorithms and streaming systems. Design event schemas, create scalable pipelines, select processing engines for fast analytics, and work with Top-K, HyperLogLog, sessionization, and error metrics. Optimize for cost, reliability, and performance with real-world patterns in this practical course.
Elevify advantages
Develop skills
- Design streaming data pipelines for low-latency workflows.
- Implement scalable metrics like HLL, Top-K, and session windows.
- Model video events with analytics-first schemas and quality.
- Optimize big data costs through storage and compute tuning.
- Harden pipelines with fault tolerance 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 say
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