Master the core algorithms and architectural patterns that power AI-scale data infrastructure
Complex concepts explained simply
Imagine organizing millions of books in a library. You need smart ways to find any book quickly, update the collection, and handle thousands of readers at once. That's what we'll teach you for data.
Like managing a busy restaurant kitchen where orders keep coming, ingredients arrive late, and you need to serve perfect dishes on time. We'll show you the recipes for data streams.
Picture building a highway system that can handle millions of cars, predict traffic jams, and reroute automatically. That's AI-scale architecture for data pipelines.
6 intensive modules covering essential algorithms
Master the algorithms that solve routing and dependency problems at scale
Imagine you're planning the best route for delivering packages to multiple cities. Johnson's Algorithm helps you find the shortest path between every pair of cities, even when some roads have tolls (negative weights). This is exactly what happens when optimizing data flow through complex pipeline networks.
All-pairs shortest path with O(V²log V + VE) complexity
Real-time path updates for streaming data
Memory optimization & fault tolerance
Distribute data evenly across thousands of servers
Think of organizing students into classrooms. When a new classroom opens or one closes, you want to move as few students as possible. Consistent hashing ensures minimal disruption when servers are added or removed, keeping your data balanced and accessible.
Google's O(1) space algorithm
Minimal disruption on node changes
Prevent hotspots in AI workloads
Build systems that can ingest millions of events per second
Like keeping a diary where you always write at the end and never erase, LSM trees let you write data incredibly fast. Later, a background process organizes everything neatly, like filing papers from your desk into proper folders.
Log-structured merge trees for high-throughput writes
Probabilistic queries with zero false negatives
Get accurate-enough answers using 1000x less memory
Instead of counting every car on a highway (expensive!), you sample traffic at key points and estimate the total. HyperLogLog and MinHash do this for data, giving you 99% accurate answers using a tiny fraction of the resources.
Count unique items in massive streams
Find similar documents at scale
Process petabytes of data efficiently
Like sorting a deck of cards that's too big for your table, external merge sort breaks the task into manageable chunks. Count-Min Sketch is like keeping a compressed summary that still answers your questions accurately.
Sort data larger than memory
Frequency estimation with bounded error
Handle out-of-order events and maintain consistency
Like organizing a race where runners might cross checkpoints out of order, watermarks help you know when all data for a time window has arrived. Snapshots let you pause and resume without losing any progress.
Track event-time progress accurately
Distributed checkpointing without stopping
Apply your knowledge to real-world challenges
Build a real optimizer that reduces pipeline execution time by finding optimal DAG paths
Create a cache system that survives node failures with minimal data movement
What you'll be able to do after this course
Solve complex routing, dependency, and optimization problems using battle-tested algorithms
Design systems that grow from 1x to 1000x without complete rewrites
Build AI-scale infrastructure with proper monitoring, fault tolerance, and optimization
The best engineers don't memorize solutions—they understand the fundamental patterns. This course teaches you to recognize when to use each algorithm, not just how they work.
This is an advanced course designed for experienced engineers. You should be comfortable with:
Level up from building features to designing systems
Master the algorithms behind your tools
Build infrastructure that scales with your models
Make informed architectural decisions
Join advanced engineers from leading tech companies in this intensive course