Big Data Algorithms for Engineering Course
Dive into big data algorithms for real-time analytics in this focused course. Explore streaming architectures, scalable metrics like HyperLogLog and Top-K, event schema design for video data, and strategies for cost-effective, reliable pipelines. Build skills to support large-scale video platforms and tech systems with precise, actionable insights from streaming data.

4 to 360 hours flexible workload
valid certificate 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 with Top-K, HyperLogLog, sessionisation, and error metrics, plus optimisation of cost, reliability, and performance via real-world pseudocode and patterns.
Elevify advantages
Develop skills
- Design streaming data pipelines for low-latency, exactly-once processing.
- Implement scalable metrics using HLL, Top-K, and session windows for KPIs.
- Model video events with analytics-focused schemas that evolve robustly.
- Optimise big data costs through storage, compute, partitioning, and compaction tuning.
- Strengthen pipelines with fault tolerance, testing, monitoring, and anomaly detection.
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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The platform is fast and easy to use. The variety of content and the extra videos really help with learning.

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