Production-grade distributed streaming, analytics lakehouse, and autonomous data infrastructure built for scale.
Architecture & SLA SpecsView Tech Specs ↓
Ingestion Pattern: PostgreSQL Write-Ahead Log (WAL) → Debezium CDC → Apache Kafka → ClickHouse Engine → Materialized Views → dbt SQL Models.
Performance SLA: Sub-2-second end-to-end sync delay from PostgreSQL write to ClickHouse queryable state under 10k events/sec transactional load.
Key Features: Exactly-once delivery semantics, automated schema evolution handling, and metadata-driven partition pruning.
Architecture & SLA SpecsView Tech Specs ↓
Streaming Pattern: Kafka Producers → Partition Router Node → Dynamic Topic Partition Dispatcher → Consumer Groups.
Performance SLA: Sub-10ms message routing latency with dead-letter queue (DLQ) fallback for malformed payload schemas.
Key Features: Runtime JSON Schema / Avro validation, consumer lag telemetry, and dynamic partition rebalancing.
Architecture & SLA SpecsView Tech Specs ↓
Cluster Pattern: Helm-deployed Kubernetes Cluster → Airflow Scheduler (KubernetesExecutor) → Spark-on-K8s Operator → Worker Pod Autoscaling.
Observability SLA: Real-time Prometheus metrics scraped from Spark Drivers/Executors and rendered on custom Grafana dashboards.
Key Features: Dynamic pod allocation based on memory pressure, automated S3/MinIO checkpointing, and zero-downtime DAG deployments.
Architecture & SLA SpecsView Tech Specs ↓
Agent Loop Pattern: Kafka Pipeline Error Event → Agent Coordinator → pgvector Semantic Search over Historical Error Embeddings → ReAct Code Patch Generation → Isolated Docker Sandbox Verification → Automated SQL/Pipeline Patch PR.
Safety SLA: 100% dry-run validation in sandboxed containers before applying patches to staging/production pipelines.
Key Features: RAG over stack trace embeddings, strict SQL AST parsing, and automated rollback triggers.