HIGAET System Design
Study how large systems scale, from load balancing and caching to queues, sharding, replication, consistency models, failure handling, and consensus protocols.
Duration
8 weeks · 6-8 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
System design is how large systems scale: load balancing, caching, queues, sharding, replication, consistency models, failure handling, and consensus protocols. It matters now because traffic spikes, regional outages, and growing data force hard trade-offs no single server can dodge.
It is used to plan high-traffic services, read-heavy platforms, and background pipelines, solving capacity planning, cache behavior, and resilient workflows. It does not solve application problems by itself: a queue does not fix unclear requirements, caching does not fix a broken data model, and diagrams do not fix untested failure handling.
By the end you will be able to build a capacity model connecting traffic estimates to servers, storage, and bandwidth, a cache hierarchy with eviction and invalidation trade-offs, and a queue-based workflow with retries, dead letters, and ordering guarantees alongside a multi-region read pattern with replication-lag and failover planning.
Why this course exists
The gap is between a whiteboard diagram that looks scalable and a production design that accounts for capacity, consistency, replication lag, retries, and failure modes. The course teaches the arc from idea to design to code to test to deploy to operate, so students can reason about scale and defend their trade-offs.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Experience building or operating backend services
- Understanding of HTTP, databases, and caching basics
- Comfort with traffic and capacity estimation math
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Module 01 — Foundations of Scale, Latency, and Availability
- Module 02
Module 02 — Load Balancing, Caching, and Content Delivery
- Module 03
Module 03 — Queues, Streams, and Asynchronous Workflows
- Module 04
Module 04 — Sharding, Partitioning, and Replication
- Module 05
Module 05 — CAP, Consistency Models, and Consensus
- Module 06
Module 06 — Storage Selection and Data Lifecycle Design
- Module 07
Module 07 — Observability, Failure Drills, and Cost Control
- Module 08
Module 08 — Capstone: Present and Defend a Scalable System Design
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build capacity models that connect traffic estimates to servers, storage, and bandwidth.
- Design cache hierarchies with eviction, invalidation, and consistency trade-offs.
- Develop queue-based workflows with retries, dead letters, and ordering guarantees.
- Deploy multi-region read patterns with replication lag and failover planning.
- Integrate sharding and partitioning strategies for hot keys and uneven growth.
- Evaluate CAP and PACELC trade-offs for session, catalog, and payment workloads.
- Secure distributed communication with mutual TLS, idempotency, and audit trails.
- Optimize consensus-dependent paths using leader election and quorum reasoning.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Capacity model linking traffic to servers and storage
- Project 02
Cache hierarchy with eviction and invalidation
- Project 03
Queue-based workflow with retries and dead letters
- Capstone
Multi-region read design with replication and failover plan
Speak the language first.
- Load balancing
- Spreading incoming traffic across servers so no single machine becomes a bottleneck.
- Cache hierarchies
- Layers of fast temporary storage from browser to content network to server that reduce repeated work.
- Eviction and invalidation
- Rules for removing cached entries when space runs out or when stored data becomes outdated.
- Message queues
- Buffers that hold tasks between services so bursts of work can be processed steadily.
- Retries and dead letters
- Automatic re-attempts for failed tasks plus a holding area for messages that keep failing.
- Sharding and replication
- Splitting data across machines for scale plus keeping copies for faster reads and recovery.
- Consistency models
- Guarantees about when different copies of data will agree, trading freshness against speed and availability.
- Capacity modeling
- Estimating servers, storage, and bandwidth from expected traffic so the design has enough headroom.
- Consensus protocols
- Methods for distributed machines to agree on one value or leader even when some parts fail.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Cache serves stale data after updates
Shorten time-to-live on fast-changing keys and add explicit invalidation on the write path.
Queue backlog grows with repeated retries
Add backoff with limited retries, route poison messages to a dead-letter queue, and alert on its growth.
One shard or replica becomes a hotspot
Rebalance keys, add read replicas for hot data, and review the partitioning scheme for skew.
Failover serves outdated reads
Check replication lag metrics, direct sensitive reads to the primary, and document acceptable lag for other reads.
Capacity estimate misses peak traffic
Rebuild the model from peak measurements with headroom, then load-test the bottleneck tier.
Before you move on, you should be able to
- Build capacity models connecting traffic estimates to servers, storage, and bandwidth
- Design cache hierarchies with eviction, invalidation, and consistency trade-offs
- Build queue-based workflows with retries, dead letters, and ordering guarantees
- Deploy multi-region read patterns with replication lag and failover planning
- Explain consistency, availability, and latency trade-offs for a design
- Evaluate failure handling for balancing, sharding, and replication choices
- Design a scaling plan for traffic growth and regional failure
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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