Core Algorithms for Modern Data Systems

Master the foundational algorithms that power AI-scale data infrastructure

📅 4-Hour Course + 2-Hour Workshop 👥 Advanced Level 🎯 Hands-On Implementation

Course Overview

This intensive program covers foundational algorithms and architectural patterns essential for building AI-scale data infrastructure. Participants will master core algorithms that power modern distributed systems and design patterns for both batch and streaming data processing at enterprise scale.

Learning Objectives

  • Implement graph algorithms for distributed routing
  • Design scalable data partitioning strategies
  • Build write-optimized storage systems
  • Apply approximate algorithms for analytics

Key Outcomes

  • Solve complex system design problems
  • Optimize data pipeline performance
  • Handle AI-scale workloads efficiently
  • Make informed architectural decisions

Prerequisites

This is an advanced course. Participants should have:

Technical Knowledge

  • ✓ Distributed systems concepts
  • ✓ Big-O complexity analysis
  • ✓ Basic graph theory
  • ✓ Database fundamentals

Experience

  • ✓ 3+ years software engineering
  • ✓ Production system experience
  • ✓ Data pipeline development
  • ✓ One programming language proficiency

Module Details

Module 1: Graph Algorithms for Distributed Systems

60 min

Master algorithms for routing and dependency resolution in distributed environments

1.1 Johnson's Algorithm (25 min)

Theory: Combining Bellman-Ford reweighting with Dijkstra's optimization

Complexity: O(V²log V + VE)

Implementation:

  • Graph reweighting to eliminate negative edges
  • Running Dijkstra's from each vertex
  • Restoring original weights

Applications:

  • Data pipeline dependency resolution
  • Multi-cloud routing optimization
  • Resource allocation in clusters

1.2 Dynamic Shortest Path (25 min)

Hybrid Approach: Bellman-Ford + incremental Dijkstra's

Key Concepts:

  • Negative cycle detection
  • Incremental updates for streaming graphs
  • Maintaining shortest path trees

Real-time Applications:

  • Live data lineage tracking
  • Circuit breaker patterns
  • Adaptive load balancing

1.3 Production Considerations (10 min)

  • Memory optimization for large graphs
  • Parallelization strategies using graph partitioning
  • Fault tolerance and checkpointing
  • Distributed graph processing frameworks

Module 2: Advanced Consistent Hashing

60 min

Implement scalable partitioning strategies for AI workloads

2.1 Jump Consistent Hash (25 min)

Core Algorithm: Google's O(1) space complexity hash

Implementation:

int32_t JumpConsistentHash(uint64_t key, int32_t num_buckets)

Mathematical Foundation:

  • Pseudo-random number generation
  • Uniform bucket assignment probability
  • Linear congruential generators

Advantages:

  • Zero memory overhead vs O(V log V) for virtual nodes
  • Perfect load balancing
  • Deterministic redistribution

2.2 Rendezvous Hashing (20 min)

Algorithm: max(hash(key, server_i)) for selection

Properties:

  • Minimal disruption: only K/N keys move
  • No memory overhead for routing table
  • Symmetric agreement without coordination

Vector Database Applications:

  • Embedding space partitioning
  • Multi-dimensional range queries
  • Shard-aware similarity search

2.3 Bounded Load Hashing (15 min)

Problem: Hotspot mitigation in skewed workloads

Solution: Viceroy algorithm with constraints

  • Track per-node load metrics
  • Redirect overflow to next viable node
  • Maintain consistency guarantees

Module 3: Write-Optimized Storage Engines

60 min

Build storage systems for high-throughput data ingestion

3.1 LSM Trees (35 min)

Architecture Components:

  • MemTable: In-memory write buffer
  • SSTables: Sorted String Tables on disk
  • Write-Ahead Log for durability

Compaction Strategies:

  • Size-tiered compaction
  • Leveled compaction
  • Time-window compaction

Write Amplification: O(log N) levels

Applications: InfluxDB, Cassandra, RocksDB

3.2 Bloom Filters (25 min)

Implementation:

  • Bit array of size m
  • k independent hash functions
  • Optimal k = (m/n) * ln(2)

False Positive Rate: (1-e^(-kn/m))^k

Integration:

  • LSM read optimization
  • Distributed query filtering
  • Cache admission policies

Module 4: Approximate Analytics at Scale

60 min

Achieve accurate-enough answers using minimal resources

4.1 HyperLogLog (30 min)

Algorithm: Probabilistic cardinality estimation

Key Concepts:

  • Leading zero counting
  • Harmonic mean estimation
  • Bias correction for small cardinalities

Accuracy: Standard error of 1.04/√m

Distributed Aggregation:

  • Merging HLL sketches: max operation
  • Network transfer: fixed size regardless of cardinality

Use Cases:

  • Unique visitor counting
  • Data profiling for ML pipelines
  • Real-time dashboard metrics

4.2 MinHash (30 min)

Jaccard Similarity: |A∩B| / |A∪B|

Algorithm:

  • Generate k hash functions
  • Store minimum hash value for each
  • Similarity ≈ matching minimums / k

LSH (Locality-Sensitive Hashing):

  • Band technique for similarity search
  • Tunable precision-recall tradeoff

Applications:

  • Near-duplicate detection
  • Recommendation systems
  • Data deduplication

Module 5: Advanced Batch Processing

30 min

Process petabyte-scale data efficiently

5.1 External Merge Sort (15 min)

Knuth's Replacement Selection:

  • Priority queue for run generation
  • Minimize number of I/O passes
  • Optimal buffer allocation

Performance:

  • Standard: O(n log n / B)
  • Optimized: O(n log(n/M) / B)
  • B = block size, M = available memory

AI Applications:

  • Sorting training datasets exceeding memory
  • Distributed sorting in Spark
  • Feature engineering on data lakes

5.2 Count-Min Sketch (15 min)

Structure: d × w matrix of counters

Operations:

  • Update: count[i][hash_i(x)] += c
  • Query: min(count[i][hash_i(x)])
  • Merge: Element-wise addition

Error Bounds:

  • ε-δ approximation guarantee
  • With probability 1-δ, error ≤ ε||a||₁

Use Cases:

  • Heavy hitter detection
  • Approximate group-by operations
  • Network traffic analysis

Module 6: Stream Processing & State Management

30 min

Handle out-of-order events and maintain consistency

6.1 Watermark Generation (15 min)

Types of Watermarks:

  • Punctuated: In-stream markers
  • Heuristic: Statistical models
  • Perfect: Oracle-based (testing only)

Implementation:

watermark = max(event_time) - max_out_of_orderness

Applications:

  • Window triggering
  • Late data handling
  • State garbage collection

6.2 Chandy-Lamport Snapshots (15 min)

Algorithm Steps:

  • Initiator records local state
  • Send markers on all channels
  • Record in-flight messages
  • Complete on marker receipt

Flink Implementation:

  • Asynchronous barrier snapshotting
  • Alignment for exactly-once
  • Unaligned checkpoints for low latency

Critical for AI:

  • Model training state recovery
  • Feature store consistency
  • Pipeline fault tolerance

Workshop: Hands-On Implementation

Part 1: Algorithm Challenges (60 min)

Exercise 1: Data Pipeline Optimizer

Build a production-ready pipeline optimizer using Johnson's algorithm

Requirements:
  • • Parse DAG dependencies
  • • Calculate optimal execution order
  • • Handle dynamic updates
  • • Minimize resource contention
Deliverables:
  • • Working implementation
  • • Performance benchmarks
  • • Test cases
  • • Optimization report

Exercise 2: Distributed Cache Simulator

Implement consistent hashing with failure recovery

Requirements:
  • • Virtual nodes implementation
  • • Node failure simulation
  • • Data rebalancing
  • • Load distribution metrics
Evaluation:
  • • Load balance factor
  • • Data movement on scaling
  • • Recovery time
  • • Memory efficiency

Part 2: Architecture Design (60 min)

Scenario: AI-Powered Recommendation Engine

Design a complete recommendation system for 100M+ users and 1B+ items

Phase 1: Data Ingestion (20 min)
  • • Design multi-source pipeline (clickstream, transactions, content)
  • • Choose partitioning strategy
  • • Define data schemas
  • • Plan for backpressure handling
Phase 2: Feature Store (20 min)
  • • Real-time feature computation
  • • Batch feature pipelines
  • • Feature versioning strategy
  • • Consistency guarantees
Phase 3: Model Serving (20 min)
  • • Low-latency inference architecture
  • • Feature lookup optimization
  • • A/B testing infrastructure
  • • Monitoring and drift detection
Deliverables:
  • ✓ Architecture diagram
  • ✓ Data flow specifications
  • ✓ Technology choices with rationale
  • ✓ Scaling strategy (1x → 10x)
  • ✓ Failure mode analysis
  • ✓ Cost optimization plan

Assessment & Certification

Evaluation Criteria

  • ◆ Algorithm implementation correctness (30%)
  • ◆ Performance optimization (20%)
  • ◆ Architecture design quality (25%)
  • ◆ Production considerations (25%)

Certificate Requirements

  • ✓ Complete all module exercises
  • ✓ Pass workshop challenges (70% min)
  • ✓ Submit architecture design
  • ✓ Participate in peer review

Note: Upon successful completion, you'll receive a verified certificate demonstrating proficiency in core algorithms for modern data systems.

Course Resources

📚 Required Reading

  • • Designing Data-Intensive Applications
  • • Streaming Systems
  • • Database Internals
  • • Course lecture notes (provided)

💻 Tools & Software

  • • Python/Java/Go (any one)
  • • Docker & Kubernetes
  • • Apache Spark/Flink
  • • Git & GitHub

🔗 Additional Resources

  • • Research paper collection
  • • Implementation repositories
  • • Performance benchmarks
  • • Industry case studies

Questions?

Get in touch with our course coordinators

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