Analytics Heavy (OLAP)

Analytics Pipeline

An OLAP-oriented analytics architecture that ingests operational changes from PostgreSQL via WAL-based CDC into Kafka, then routes them to a columnar analytics store (ClickHouse or Snowflake) for product analytics, business intelligence, and operational reporting. The CQRS separation ensures analytical queries never degrade transactional write performance, and materialized views provide pre-aggregated query acceleration for the most expensive analytical patterns.

AI / RAG Application

A low-latency feature serving platform for ML model inference, providing both batch (offline) and real-time (online) feature access with strict training-serving consistency. Redis serves the hot feature cache with p99 latency targets below 5ms for online inference requests; PostgreSQL provides point-in-time feature lookups for offline training jobs with temporal consistency guarantees; Cassandra stores high-cardinality feature entities at write scale beyond PostgreSQL's single-primary ceiling; Kafka streams feature computation events from online feature pipelines to update the cache; Qdrant stores vector features for embedding-based model inputs and similarity lookups; ClickHouse serves feature analytics and drift monitoring across training dataset populations. Feature versioning is first-class: every feature value is tagged with a pipeline_version and computed_at timestamp to support model reproducibility and training-serving skew diagnosis.

Analytics Pipeline

A metrics, logs, and traces ingestion and query platform built to absorb the telemetry output of a production system fleet: including the telemetry volume spikes that accompany the incidents the platform is meant to detect. ClickHouse stores metrics data with automatic time-based rollup via continuous materialized views; TimescaleDB provides complementary time-series storage for high-cardinality alert evaluation; Kafka buffers the ingestion stream against downstream write pressure, decoupling ingest acceptance rate from storage write throughput; Elasticsearch serves log full-text search and structured field filtering; Redis caches dashboard query results and active alert state for sub-100ms alert evaluation latency. The alert engine evaluates threshold and anomaly rules against pre-computed materialized views, not raw data, to bound alert evaluation cost independent of ingestion volume.