Škálování real-time analytiky na 10M událostí/den
E-commerce platforma potřebovala real-time dashboard pro monitoring user behavior. Stack: Apache Kafka (3-node cluster) pro event ingestion, Apache Flink pro stream processing (windowed aggregations, stateful operations), ClickHouse pro OLAP queries. Výzvy: handling late events, exactly-once semantics, backpressure management. Řešení: watermarks pro event time processing, idempotent operations, dynamic scaling based on lag. Implementace trvala 3 měsíce, cost reduction 40% oproti předchozímu batch řešení.
Migrace monolitu do event-driven architektury
FinTech aplikace s 500K denními transakcemi přešla z monolitu na mikroservisy. Postupná migrace: strangler fig pattern, event sourcing pro audit trail, CQRS pro read/write separation. Stack: Spring Boot mikroservisy, Kafka pro messaging, PostgreSQL (write model), Elasticsearch (read model). Lessons learned: začít s ne-kritickými službami, dual writes rizika, kompenzační transakce, monitoring complexity. Migrace 18 měsíců, 99.95% uptime zachováno, deployment frequency 10x vyšší.
PostgreSQL optimalizace: 100TB databáze, 50K QPS
SaaS platforma s multi-tenant PostgreSQL databází čelila performance issues. Implementace: table partitioning (range by month), connection pooling (PgBouncer), query optimization (EXPLAIN ANALYZE based), index tuning (covering indexes, partial indexes), autovacuum tuning. Hardware: NVMe SSD, 768GB RAM, CPU pinning. Results: query latency P95 snížena z 500ms na 50ms, connection pool efficiency 90%, disk I/O utilization optimalizována. Cost per query sníženy o 60%.
Modernizace Data Lake: Petabyte-scale na AWS
Media company migrovalo legacy Hadoop cluster (2PB dat) do AWS S3 data lake. Architecture: S3 (storage), AWS Glue (catalog + ETL), Amazon Athena (ad-hoc queries), Amazon EMR (Spark jobs), Delta Lake (ACID transactions). Partitioning strategie: Hive-style partitioning by date, columnar format (Parquet), Z-ordering for frequently filtered columns. Cost optimization: S3 Intelligent-Tiering, Spot instances pro EMR, query result caching. TCO reduction 55%, query performance 3x rychlejší.
Production ML Pipeline: Feature Store + Model Serving
Recommendation engine processing 1B predictions/day. Stack: Feast (feature store) pro feature management a serving, Kubeflow Pipelines pro training orchestration, TensorFlow Serving + KServe pro model deployment, MLflow pro experiment tracking. Challenges: feature freshness (real-time features vs batch), model versioning, A/B testing infrastructure, monitoring (data drift, model performance). Results: time-to-production nových modelů 2 týdny → 2 dny, feature reuse 70%, prediction latency P99 < 100ms.