Hands On System Design Course - Code Everyday
Build a complete, production-ready distributed log processing system from scratch. Each day features practical, hands-on tasks with concrete outputs that incrementally develop your expertise in distributed systems architecture, scalable data processing.
Day 158: User Behavior Analytics - Catching the Insider Threat
The post outlines building a User Behavior Analytics (UBA) system that learns normal employee activity and flags anomalies in real time. By establishing a behavioral baseline, the solution can spot insider threats such as off‑hours server access or sudden data‑exfiltration spikes. It positions UBA as a “behavioral psychologist” for IT environments, turning raw logs into risk scores. The approach promises faster detection and more precise alerts compared with traditional rule‑based security tools.
Day 52: Implement a Simple Inverted Index for Log Searching
The post walks through building a real‑time inverted index for log data, ingesting messages from Kafka, tokenizing them, and persisting the index in Redis for hot lookups and PostgreSQL for cold storage. It adds a search API that ranks results...
Day 157: Building Intelligent Threat Detection Rules - Your Security Autopilot
The post walks readers through building a production‑ready threat detection rule engine that can ingest more than 1,000 logs per second, identify over 15 common attack patterns, and issue real‑time alerts with zero false negatives for critical threats. It uses...
Day 51: Build Dashboards for Visualizing Analytics Results
The post outlines how to build a real‑time analytics dashboard that consumes aggregated metrics from Kafka streams and pushes updates via WebSockets. It highlights a query‑optimization layer that combines Redis caching with PostgreSQL time‑series partitioning to keep latency sub‑second. Multi‑dimensional...
Day 156: Building Your Security Command Center - SIEM Implementation
The post walks security leaders through building a Security Information and Event Management (SIEM) platform tailored for a financial services firm handling millions of transactions daily. It outlines how raw logs—from user logins to network traffic—are normalized, correlated, and scored...
Day 50: Alert Generation Based on Log Patterns
The post outlines a production‑grade distributed alerting platform that watches log streams in real time and fires intelligent notifications. It leverages a Kafka Streams‑based rule engine capable of processing over 50,000 events per second, a smart manager that deduplicates, correlates...
Day 155: Building Smart Capacity Planning Tools
The post outlines a full‑stack capacity‑planning system that ingests historical log metrics, applies time‑series analysis, and forecasts resource needs 7‑30 days ahead. It details a five‑component architecture—collector, analyzer, forecasting engine, resource calculator, and dashboard—using linear regression, exponential smoothing, and Prophet‑style...
Day 49: Implement Anomaly Detection Algorithms for Distributed Log Processing
The post outlines a production‑grade anomaly detection system for streaming log data, combining Z‑score and IQR statistical filters, time‑series baseline analysis, multi‑dimensional clustering, and adaptive thresholds. It emphasizes sub‑second latency and horizontal scalability, referencing Netflix’s 800‑service monitoring, Uber’s 100,000‑event‑per‑second fraud...
Day 154: Building Bulletproof Disaster Recovery for Distributed Log Systems
Financial services firms processing millions of log events per second need instant recovery when a data center fails. The blog post walks through building a production‑grade disaster‑recovery system that automates detection, failover, and validation with concrete RTO (2 minutes) and RPO...
Day 48: Sessionization for User Activity Tracking
The post outlines a production‑grade sessionization pipeline that turns raw event streams into actionable user sessions using Kafka Streams session windows, a Redis‑backed active‑session cache, and PostgreSQL for persistence. It highlights real‑time session tracking with sub‑millisecond lookups and a REST...
Day 153: Unified Infrastructure & Log Monitoring - The Complete Observability Picture
The post introduces a unified observability solution that merges infrastructure metrics with application logs across a 50‑pod Kubernetes cluster. It walks readers through building a collector, real‑time dashboard, and intelligent alerting that ties CPU, memory, network, and disk data to...
Day 152: Building a Custom Kubernetes Operator for Log Platform Management
The post walks readers through building a custom Kubernetes operator to manage a distributed log‑processing platform, automating deployment scaling, configuration updates, health monitoring, and failure recovery. It outlines the operator pattern, CRD design, reconciliation loops, and real‑time dashboards, citing Spotify...
Day 46: Time-Based Windowing for Real-Time Log Aggregation
The post walks through building a production‑grade time‑based windowing engine for real‑time log analytics, covering tumbling, hopping and session windows, a metrics calculator, late‑data handling, and RocksDB‑backed state persistence. It demonstrates sub‑100 ms latency while processing over 50,000 events per second...
Day 45: Implement a Simple MapReduce Framework for Batch Log Analysis
The post outlines a production‑grade MapReduce framework that handles a full map‑shuffle‑reduce pipeline for batch log analysis, processing millions of events. It features a coordinator‑worker model with automatic task retries and a partitioned storage backend for efficient shuffling. While Kafka...
Day 150: Multi-Cloud Deployment Templates - Making Your Log System Cloud-Native
On day 150 the author shifts focus from building a high‑throughput log processing system to shipping it via multi‑cloud Infrastructure as Code templates. The IaC blueprints enable a single‑command deployment to AWS, Azure, or Google Cloud, turning containers, databases, caches,...
Day 44: Real-Time Monitoring Dashboard with Kafka Streams
The post walks through building a production‑grade real‑time monitoring dashboard that ingests over 40,000 events per second using Kafka Streams. It shows how windowed aggregations, percentile calculations, and anomaly detection run on RocksDB‑backed state stores with exactly‑once guarantees. The stream...
Day 149: Orchestrating Your Log Processing Empire with Kubernetes
The post walks readers through turning a complex, distributed log‑processing stack—collectors, RabbitMQ, query engines, and storage—into a single Kubernetes deployment. By providing complete manifests, it shows how to launch the entire ecosystem with one command, while Kubernetes handles health checks,...
Day 43: Implement Log Compaction for State Management
The post outlines a production‑grade state management layer built on Kafka log‑compacted topics, featuring a keyed state producer, a consumer that materializes current snapshots, and a Redis‑backed query API. By retaining only the latest record per entity key, log compaction...
Day 148: Natural Language Queries with NLP - Ask Your Logs Anything
The blog announces a natural language query engine for log platforms, letting users ask questions like “show me errors from payment service in the last hour” and receive instant results. By converting conversational intent into optimized SQL, the system removes...
Day 42: Exactly-Once Processing Semantics in Distributed Log Systems
The post details a new Kafka‑based log pipeline that guarantees exactly‑once processing, eliminating duplicate handling even during failures. It combines idempotent producers, transactional consumer commits, a Redis‑backed deduplication layer, and a state‑reconciliation service to create an end‑to‑end exactly‑once flow. The...
Day 146: Time Series Database Integration - Turning Logs Into Queryable Metrics
Today's post highlights the shift from raw log files to queryable metrics using time‑series databases. It explains why traditional relational databases falter with high‑write, append‑only workloads and showcases InfluxDB and TimescaleDB as purpose‑built solutions. The article illustrates how these databases...